The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.  •AI and AIoT hold significant potential to address complex environmental challenges.•AI and AIoT are being applied in smart cities to enhance their performance and efficiency.•The concept of smarter eco-cities is based on the groundbreaking convergence of AI and IoT.•Smarter eco-cities are leveraging AI and AIoT to advance their environmental sustainability goals.•AI and AIoT pose environmental, technical, ethical, social, and regulatory challenges. This surge has led to the emergence of smarter eco-cities, where Artificial Intelligence of Things (AIoT) takes center stage and garners significant attention. In this context, AIoT is poised to offer innovative solutions to the mounting environmental challenges confronting smart cities and, by extension, smart eco-cities. AIoT holds the potential to unlock avenues for improving resource efficiency, reducing energy consumption, streamlining waste management, enhancing transportation management, conserving biodiversity, and mitigating environmental impacts. These advancements hold the power to reshape the urban development landscape in response to the surging wave of urbanization and the increasing complexity of ecological degradation, transforming cities into hubs of intelligence, sustainability, and environmental consciousness.AI is rapidly reshaping both the technological and urban landscapes, serving as a pivotal driving force behind the advancement of smart cities and smart eco-cities . Its poThe centralized infrastructure of IoT is grappling with significant challenges, primarily driven by the extensive strain imposed by the immense volume of data being generated and processed. Harnessing actionable insights from these data necessitates the integration of AI models and algorithms to effectively manage the data flow, storage, and processing inherent in the IoT infrastructure. The emergence of AIoT is underpinned by various factors that set it apart from traditional IoT. To begin with, AIoT capitalizes on the synergies between AI and IoT technologies, facilitating more intelligent and efficient data processing, analysis, and decision-making . ThrougSimilar to AI, AIoT has become integral to the functioning of smart cities and, hence, smarter eco-cities. Notably, it has demonstrated innovative potential in addressing complex environmental challenges. Recent research has concentrated on the practical applications of AI and AIoT across various domains of environmental sustainability and climate change . TowardAmid a world increasingly beset by uncertainties, compounded by the urgency of the climate crisis and the rapid digital transformation of smart cities and eco-cities, catalyzed by the emergence of AI and AIoT technologies, a compelling trajectory is emerging. This trajectory envisages the integration of these technologies' applied solutions into smart eco-cities to deal with the complexity of environmental degradation and climate disruption — under what can be termed as “smarter eco-cities.” This visionary concept entails the implementation of innovative, forward-looking strategies to reshape the urban landscape of the future. However, it is worth acknowledging that while AI and AIoT technologies hold immense potential yet to unlock, they pose environmental risks and amplify a spectrum of societal, ethical, legal, and regulatory challenges.•Describe, illustrate, and make meaningful connections between the fundamental concepts underpinning emerging smarter eco-cities.•Present the existing work in the field and explain how the present study differs from it and why this is a “step forward” and a new contribution to knowledge.•Analyze, synthesize, interpret, and critically evaluate the existing knowledge in the areas of environmental sustainability, climate change, and smart cities to derive comprehensive insights into the role of AI and AIoT solutions in the advancement of emerging smarter eco-cities.•Categorize AI and AIoT solutions based on these three areas and further evaluate their impact on advancing environmental sustainability goals.•Capture the dynamic landscape of AI and AIoT solutions for these three areas by identifying emerging trends, innovations, and novel approaches within the framework of smarter eco-cities.•Identify and discuss the key challenges and barriers that arise when implementing AI and AIoT solutions in the development of emerging smarter eco-cities.•Identify existing gaps and explore relevant avenues for future research and areas requiring further investigation.The body of literature exploring AI and AIoT solutions within the realm of environmental sustainability, climate change, and smart cities is rapidly expanding. Nonetheless, to the best of our knowledge, no review study has systematically analyzed and synthesized the existing corpus of knowledge regarding the interconnections and synergies between these three domains, coupled with their intersection with smart eco-cities with respect to AI and AIoT technologies and solutions. Furthermore, while a few recent review studies have taken a broader perspective on smart eco-cities, none have ventured into exploring their emerging technological and environmental solutions from an integrative perspective. Bridging these gaps, the present study embarks on a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework that seamlessly integrates configurative, aggregative, and narrative synthesis approaches. To achieve the overarching aim, the study focuses on the following specific objectives:RQ1: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies?RQ2: What are the key drivers and enablers propelling the materialization of smarter eco-cities?RQ3: What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?RQ4: In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer in the realm of smarter eco-cities?RQ5: What challenges and barriers arise when implementing AI and AIoT solutions for the development of smarter eco-cities?By pursuing these specific objectives, the study endeavors to provide a holistic and in-depth understanding of the integration of AI and AIoT solutions within emerging smarter eco-cities, ultimately contributing to the advancement of environmental sustainability practices in urban contexts. Toward this end, it pursues the following research questions:•Uncovering the dynamjc interplay between urbanism paradigms, environmental solutions, and data-driven technologies in emerging smarter eco-cities.•Identifying the driving forces behind the materialization of smarter eco-cities, namely technological advancements, environmental concerns, and policy instruments.•Examining the multifaceted roles of AI and AIoT in environmental sustainability, climate change, and smart cities and how these technologies can be harnessed in the development of smarter eco-cities.•Exploring the specific ways in which AI and AIoT technologies propel sustainable development pracrices and advance environmental goals.•Highlighting best practices where AI and AIoT solutions can significantly contribute to improving the environmental sustainability of smarter eco-cities.•Identifying and evaluating a spectrum of challenges linked to the implementation of AI and AIoT in smarter eco-cities, aiming to illuminate potential obstacles and devise strategies to mitigate or overcome them.•Unveiling uncharted territories and encouraging the exploration of AI and AIoT solutions to drive large-scale implementations of smarter eco-cities, propelling the discourse forward and fostering innovation in sustainable urban development.•Providing a visionary outlook by elucidating how AI and AIoT technologies contribute to sustainable urban futures and exploring the transformative potential of these technologies in redefining urban infrastructures and systems.By synthesizing the existing evidence and analyzing the state-of-the-art research to answer these research questions, the systematic review contributes to consolidating, enhancing, and transforming the existing knowledge on smart eco-urbanism by:In essence, the study not only presents a comprehensive overview of the current landscape of smarter eco-cities but also highlights the potential of AI and AIoT technologies in shaping the future of sustainable urban development, in addition to providing a roadmap for advancing the discourse on smarter eco-cities and facilitating interdisciplinary collaborations. Moreover, the applied unified evidence synthesis approach offers a more holistic and nuanced understanding of the research topic addressed by enhancing the thoroughness, depth, and breadth of the systematic review. The insights derived from the systematic review will not only inform researchers and practitioners in the field but also guide policymakers and practitioners in making informed decisions regarding the adoption and implementation of AI and AIoT technologies in sustainable urban management and planning. Overall, by highlighting the solutions, opportunities, benefits, and challenges in the field of smarter eco-cities, the systematic review will further facilitate the advancement of research, policy, and practice in pursuing more sustainable and technologically advanced urban environments.This study is structured as follows: Section 2 introduces, describes, and illustrates the key conceptual strands of the study. Section 3 addresses the research review related to the study. Section 4 describes and illustrates the methodology applied in the study. Section 5 presents the results of the literature analysis and synthesis. Section 6 provides a detailed discussion, covering key challenges, open issues, and limitations. Section 7 identifies relevant gaps and presents recommendations for potential research directions and areas that require more exploration. This study concludes, in Section 8, with a summary of key findings and implications.2Key relevant concepts need to be clarified together with their integrative and synergistic facets. The value of linking these concepts  lies in 2.12.1.1Smart cities have gained significant attention as a potential solution to address sustainability, resource management, and urbanization challenges. Numerous attempts have been made to define the concept of smart cities. They suggest many definitions and a plethora of directions to smart city development . The coHowever, smart cities face several challenges that need to be addressed to ensure their successful implementation as well as their integration with other emerging paradigms of urbanism. As mentioned earlier, one of the primary problems in this regard is the lack of a standardized definition for smart cities. This lack of clarity has led to confusion and inconsistency in the planning and implementation of smart city initiatives. Moreover, the existing smart city infrastructures are not designed to support the integration of advanced technologies and data-driven systems. They need to support the connectivity, data collection, and efficient management of resources, which involves scalability and interoperability. As smart cities grow and more devices and sensors are connected, their infrastructure must handle the increasing volume of data and the growing number of users. Integration and seamless communication between different systems and devices are crucial for the smooth functioning of their infrastructure. In addition, with the extensive use of data and connected devices in smart cities, ensuring the security and privacy of their infrastructure becomes paramount. These infrastructures must have robust security measures in place to protect against cyber threats and safeguard the privacy of citizens' data. Several studies  high2.1.2The concept of eco-cities refers to “an urban environmental system in which input (of resources) and output (of waste) are minimized,” . With th2.1.3Eco-cities manifest themselves into different models based mainly on applying the principles of urban ecology or combining the strategies of sustainable cities and the solutions of smart cities. For the latter, the most prominent of those models are smart eco-cities, which integrate IoT and Big Data technologies with environmental technologies for achieving urban sustainability . Smart Unlike smart cities, smart eco-cities go beyond technology-driven approaches and emphasize environmental sustainability and ecological balance as central pillars. They focus on the integration of sustainable practices and advanced technologies, incorporate nature-based solutions into urban planning and design, and engage communities in environmental stewardship. They aim to create harmonious urban environments that not only leverage technology but also prioritize preserving and enhancing natural resources, biodiversity, and ecosystem services. In essence, they strive for a more holistic and nature-centric approach to urban development, promoting long-term sustainability and resilience and preserving natural resources for future generations to create environmentally friendly and livable urban communities.2.1.4•The innovative potential of AI and AIoT technologies for sustainability;•The enhanced sustainability outcomes enabled by the applied AI and AIoT solutions;•The synergies between smart city systems and eco-city systems;•The optimized performance and efficiency of smart eco-city systems; and•The improved practices of urban management and planning.The concept of smarter eco-cities, as specific to this study, describes smart eco-cities that integrate AI and AIoT technologies and solutions with environmental technologies and strategies to maximize the performance of their sustainable systems and integrate them with smart systems given the clear synergies in their operation. This integration is intended to produce combined effects greater than the sum of the separate effects of these systems in terms of boosting the benefits of environmental sustainability. Smart city systems include smart grids, smart traffic lights, smart mobility, smart buildings, smart waste management, and smart environmental monitoring. Smarter eco-cities represent an evolution or advancement of smart eco-cities. They prioritize environmental sustainability, employing cutting-edge technologies and innovative approaches to energy, waste, water, transportation, and urban planning to create more resilient, sustainable, and technologically advanced urban environments. They also emphasize a more holistic and comprehensive approach to urban development by integrating the environmental, social, and economic dimensions of sustainability. Accordingly, they aim to balance environmental preservation, social equity, and economic prosperity. They leverage the advanced technologies and solutions offered by smarter cities, notably AI and AIoT, to optimize urban systems and address complex challenges in a more integrated and intelligent manner. They are characterized by — as derived based on the synthesized studies:In sum, while smart cities focus on technology-driven urban development, smart eco-cities place a stronger emphasis on achieving environmental sustainability through IoT and Big Data technologies. Smarter eco-cities go a step further by incorporating social and economic dimensions, leveraging emerging AI and IoT technologies for a more holistic approach to urban development.2.22.2.1The term “IoT” describes the collective network of physical objects embedded with sensing, processing, communication, and actuating technologies and capabilities that enable them to exchange data with other devices over the Internet or other networks. These objects, often called “smart devices,” can range from everyday items to complex systems like city infrastructure. IoT allows these devices to communicate and interact with each other, collect data, transfer data, and perform automated tasks, leading to enhanced efficiency, performance, and sustainability in various urban domains. More and more IoT devices are connected worldwide daily and feeding vast amounts of data into analytical systems. It is estimated that 2.5 quintillion bytes of data are being generated globally daily, which will rise to 463 exabytes by 2025 .Edge, fog, and cloud computing are three interconnected paradigms that play a pivotal role in the IoT ecosystem in smart cities and smart eco-cities in the context of environmental sustainability . Edge coBig Data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed using traditional data processing techniques. They are huge in volume, high in velocity, diverse in variety, exhaustive in scope, fine-grained in resolution, and relational, among others. Big Data analytics involves using advanced techniques and technologies to extract valuable insights, patterns, and correlations from large and complex datasets. This process typically involves data collection, storage, processing, analysis, and visualization. In summary, IoT and Big Data are interconnected concepts revolutionizing how we collect, analyze, and utilize data. IoT enables the connectivity of smart devices, allowing them to generate vast amounts of data, while Big Data provides the means to manage, analyze, and derive meaningful insights from this data. Together, they have the potential to drive innovation, improve decision-making, and create new opportunities in diverse fields, including urban development .2.2.2AI is often described as mimicking human intelligent behavior by creating computers or machines capable of its simulation. The working definition for this study describes AI as “any device/system that perceives its environment and takes actions for its goals” . BroadlyFor example, NLP enables computers to understand, analyze, manipulate, and generate human language. NLP plays a significant role in smart cities by enabling efficient and effective communication between humans and smart systems. NLP techniques analyze and understand human language in various forms, allowing for intelligent interactions and decision-making. In smart cities, NLP can be applied in multiple domains, such as smart planning, smart governance, smart mobility, and smart services. By leveraging NLP, smart cities can enhance communication channels, improve service delivery, and gain valuable insights from citizen feedback, leading to more responsive and citizen-centric urban environments. Tyagi and Bhushan  explore Furthermore, AI involves many techniques that have gained traction over the past few years as part of AI and AIoT applications for environmental sustainability, climate change, and smart cities. These techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Regression (LR), Decision Trees (DT), Random Forests (RF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Batch-Normalization (BN), Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Genetic Algorithm (GA). As regards ML, among the supervised learning techniques used for regression, classification, or both are LR, Generalized Linear Models (LGM), DT, RF, SVM, ANN, and Bayesian Networks (BN). As to DL, it is a biological neural network or brain-inspired type of ML that uses DNN, CNNs, and Recurrent Neural Networks (RNNs) algorithms. Thus, it emulates the way humans gain certain types of knowledge by collecting, analyzing, and interpreting large amounts of data and making decisions in a faster and easier manner. DL techniques leverage neural networks comprising three fundamental layers: the input layer, hidden layers, and the output layer. These layers play a crucial role in acquiring data representation and establishing connections across multiple levels of abstraction.2.2.3To manage and analyze the dynamic and relational data generated via IoT increasingly requires powerful computational and analytical capabilities. This has led to the emergence of AIoT, a technological framework that optimizes the efficiency of IoT operations, improves human-machine interactions, advances data management and analytics models, and enhances decision-making processes. AIoT involves connecting and combining IoT devices and sensors with AI models and techniques to enable advanced analysis, enhanced real-time insights, intelligent decision-making, and autonomous behavior. It acts through control and interaction to respond to the dynamic environment, a process where ML/DL has shown value in enhancing control accuracy and facilitating multimodal interactions . The synAIoT enables the utilization of AI to incorporate intelligence and decision-making capabilities into IoT systems and applications. The AI/AIoT-driven system consists of five pillars: (1) sensing, (2) perceiving, (3) learning, (4) visualizing, and (5) acting. This is illustrated in An AI/AIoT-driven system is characterized by the ability to process raw data to extract useful insights to enable better decisions and/or take action. This involves different interrelated computational capabilities and processes. Machine perception is the capability of the system to use input data from sensors  to deduce different facets of the world, e.g., object detection/tracking, action recognition, image classification, semantic segmentation, language recognition, and pose estimation. There are different sensory information that provides patterns to the system for it to generate perceptions. Overall, machine perception aims to translate these data into meaningful information, thereby recognizing and interpreting these data by capturing the sensory information to relate to the real world. The acquisition of sensory data from the surrounding environment and their correct interpretation are key inputs to the learning process. The common state-of-the-art method for learning is based on ML, which allows the system to learn from experience without explicitly being programmed. Learning starts with collecting and preparing data  to be used as training data, building the ML model to be trained on these data, supplying the data, and letting the system train itself to find patterns or make predictions. The outcome is an ML model that can be used with different sets of data and can use the data for predictive, descriptive, prognostic, and prescriptive functions. For example, the latter function involves suggesting what actions to take.Further, there are three subcategories of ML: (1) supervised learning (trained with labeled data sets by humans to identify objects or things), (2) unsupervised/semi-unsupervised learning (finding patterns in unlabeled data such as behaviors or trends), and (3) reinforcement learning . Ullah et al.  illustraML is about leveraging data to improve performance on complex tasks by building decision-making models . These advancements can improve resource efficiency, reduce environmental impact, and promote sustainable waste management practices. The review studies on biodiversity and ecosystem services provide insights into the importance of biodiversity conservation and the role of ecosystem services in sustaining human well-being. These studies emphasize the need for conservation measures, habitat restoration, and the integration of ecosystem services into decision-making processes for sustainable development. In the context of sustainable transportation, studies shed light on various aspects of sustainable transportation, including electric vehicles, intelligent transportation systems, and multimodal transportation options. These studies highlight the potential of sustainable transportation solutions in reducing CO3.2Some reviews have been conducted on the link between AI, IoT, and Big Data in smart cities from a more general perspective 3.3Few review studies have been carried out on AIoT as an emerging technological area. Mukhopadhyay et al.  highlighThe previous review studies on AI and environmental sustainability, AI and climate change, AI and smart cities, and AIoT have laid a strong foundation for understanding the opportunities, benefits, and challenges in creating environmentally sustainable and technologically advanced urban environments. However, with the rapid advancement of AI and AIoT technologies, there is a need to explore the specific intersection of these technologies and existing smart eco-cities and their underlying multifaceted dimensions. The emerging field of AI and AIoT presents new possibilities for tackling the complexity of ecological degradation and the climate crisis in urban areas. By conducting a comprehensive systematic review of emerging smarter eco-cities and their leading-edge AI and AIoT solutions, we can bridge the gap between these solutions and the existing research on environmental sustainability, climate change, and smart cities within the defining context of smarter eco-cities. Overall, this review is the first of its kind and seeks to bring new insights into the flourishing field of smart eco-urbanism and extend the knowledge of its diverse domains by synthesizing a plethora of studies from multiple sources and disciplines.4A systematic literature review addressed the three questions guiding the study and, hence, achieved its specific objectives. It involves retrieving, mapping, aggregating, configuring, and critically evaluating studies published to address and discuss the research topic of smarter eco-cities as an interdisciplinary field . It alloRegarding stages 2 and 3, we followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach for literature search and selection . Fig. 4 The reviewed articles were published in prominent journals and conferences in urban planning, sustainable urban development, computing, and emerging technologies. Among these outlets were “Sustainable Cities and Society,” “International Conference on Smart Sustainable Cities,” “IEEE Transactions on Sustainable Computing,” “Environmental Modeling and Software,” “Cleaner Production,” “Environment and Urban Systems,” “Renewable and Sustainable Energy Reviews,” “Applied Energy,” “Sustainability,” and “Technological Forecasting and Social Change.” These outlets showcased the relevance and significance of the research in advancing the understanding and implementation of AI and AIoT solutions in the context of smart eco-cities.The literature search was conducted in late March 2023 and returned 455 documents covering 2015 to 2023. The starting year was selected because it marked the approval of the 2030 Agenda for Sustainable Development by the United Nations General Assembly as an international policy framework for the 17 SDGs. The full period, 2015–2023, captures the multifaceted nature of the topic of smarter eco-cities from the perspective of AI and AIoT technologies concerning environmental sustainability, climate change, and smart cities. This is determined by an earlier bibliometric study conducted by Bibri et al. [Data extraction and synthesis are crucial steps in conducting a systematic review. These steps involve extracting relevant information from the included studies and synthesizing the data to identify themes and patterns. Concerning stage 4, we developed a structured Excel spreadsheet that specifies what information needs to be extracted from these studies, following a deductive approach to content analysis. This information included study characteristics, methodological approaches, technological and sectoral domains, AI models and techniques applied in environmental sustainability and climate change, applied AI and AIoT solutions in smart cities, linkages between smart cities and smart eco-cities, use cases and applications, and knowledge gaps, among others. In terms of methodological approaches, for example, studies were qualitative based on descriptive analysis and literature review, mixed-methods, and quantitative approaches based on modeling and simulation regarding AI and AIoT. Moreover, we evaluated the quality and relevance of the selected documents through a critical appraisal process. This involved analyzing the methodologies used in these documents to assess the strengths and weaknesses of the research approaches. During data extraction, we carefully read and analyzed each included study to identify and record the relevant information.As regards stage 5 and 6, the primary focus of this study was to identify, make, establish, and project interconnections between the different dimensions of smarter eco-cities based on the synthesized studies. The synthesized analysis involved integrating the extracted data from multiple studies to identify patterns, trends, similarities, and differences across studies to generate findings or conclusions. The synthesis approach applied seamlessly integrated configurative, aggregative, and narrative synthesis as qualitative analytical approaches. Themes were derived from the research objectives and findings from the reviewed studies. The synthesis was performed based on these themes using an integrated approach. Specific to this study, this approach attempted to strike a balance between theoretical, empirical, and practical issues. Accordingly, it included evidence from case studies, exploratory studies, observational studies, experimental studies, theory-building studies, statistical modeling studies, and review studies. The findings of the synthesized studies were merged based on a set of specified conceptual and descriptive categories. This process entailed integrating and fusing information from the multiple studies reporting on the different dimensions of emerging smarter eco-cities and related direct and indirect linkages. This categorization evolved as more precise themes were identified and revised . From the identified categories, themes were organized to offer new interpretations beyond the synthesized studies' findings using three different — yet complementary — approaches to synthesis .Fig. 5A Configurative synthesis involves interpretation during the synthesis process to identify the big picture and construct the overall meaning, i.e., thematic synthesis . It iden5To present the results, we consolidate all pertinent information concerning emerging smarter eco-cities as an interdisciplinary field. This consolidation encompasses a wide spectrum of theoretical, empirical, and practical evidence and pertains to the various dimensions of smarter eco-cities, highlighting their synergies in producing and enhancing environmental sustainability benefits. 5.15.1.1•Power grids: to deliver energy and manage its production, consumption, and distribution to reduce costs and increase the reliability of energy supply .•Environmental management: to manage natural resources and related infrastructure to improve environmental sustainability •Transportation management: to optimize transport efficiency and manage mobility by taking into account traffic conditions and energy usage .•Waste management: to collect, recycle, reuse, recover, and dispose of different types of waste .To become environmentally smarter and more sustainable, both smart cities and eco-cities have undergone large-scale digital transformation enabled by the convergence of IoT, Big Data, and AI technologies. This has occurred at varying degrees and in different periods, given the specific focus of these two paradigms of urbanism in terms of strategies, solutions, and policies. Accordingly, in the early 2010s, numerous studies addressed the role of ICT in tackling the challenges of environmental sustainability in the realm of smart cities in various domains, notably:ICT enables the implementation of smart grids, which use advanced sensors, communication networks, and data analytics to optimize energy production, consumption, and distribution. This helps reduce costs, improve energy efficiency, integrate renewable energy sources, and enhance the reliability and resilience of the power grid. Moreover, ICT tools and systems facilitate the monitoring, analyzing, and managing of natural resources and related infrastructure. With sensors, remote sensing technologies, and data analytics, environmental parameters such as air quality, water quality, and waste management can be monitored and controlled in real time, enabling more effective environmental sustainability practices. Also, ICT applications contribute to optimizing transport efficiency and managing mobility in smart cities. Intelligent transportation systems, traffic management systems, and real-time data analysis help monitor traffic conditions, optimize routes, and improve energy usage. This leads to reduced congestion, better transportation planning, and reduced energy consumption and GHG emissions. Furthermore, ICT solutions are utilized to improve waste management processes. These include systems for waste collection, recycling, and disposal and technologies for waste monitoring, sorting, and tracking. By optimizing waste management operations and promoting recycling and resource recovery, ICT enables more sustainable and efficient waste management practices. ICT has played a vital role in transforming cities into smart and sustainable environments by enabling key advancements to achieve greater environmental sustainability and resilience in urban areas.Concurrently, smart cities started to focus on embedding the next–generation of ICT into everyday objects and city structures and systems as part of the early deployment of IoT , paDuring the period 2012–2015, smart cities gained traction as a model for sustainable urban development, with great potential to improve environmental sustainability based on IoT and Big Data Technologies . This t5.1.2It is until around 2014 that smart cities started to have a significant impact on eco-cities for environmental sustainability  traces To expand on the relationship between smart cities and eco-cities from an empirical perspective, Bibri and Krogstie  examine 5.1.3•Government regulations: The government enacts regulations that mandate specific sustainability standards and requirements for smart eco-city development, e.g., setting energy efficiency targets for buildings, enforcing waste management practices, and promoting renewable energy integration.•Financial incentives: Governments and local authorities provide financial incentives to encourage the adoption of smart technologies and sustainable practices, including tax incentives, grants, and subsidies for smart eco-city projects that incorporate smart solutions.•Public-private partnerships: Governments can partner with technology and energy companies, research institutions, and industry stakeholders to develop and implement sustainable solutions. These partnerships leverage expertise, resources, and funding to implement smart applications successfully.•Open data initiatives: Governments can promote sharing data collected from various sources, such as sensors and IoT devices, to foster innovation and facilitate evidence-based decision-making. These initiatives enable researchers, businesses, and policymakers to access and analyze information to develop smart solutions and monitor the performance of smart eco-city projects.•Standards and certification programs: Establishment of these programs ensures the interoperability, reliability, and safety of smart solutions in smart eco-cities. Standards organizations and certification bodies can define technical requirements, data privacy guidelines, and cybersecurity protocols to promote trust and facilitate the adoption of advanced technologies in smart eco-cities.The materialization of smart eco-cities is strongly influenced by, in addition to technological advancements and environmental concerns, policy instruments. Technological advancements provide the tools and capabilities for these cities to integrate and optimize various urban systems for sustainability. Environmental concerns drive the need for these cities to adopt smart and eco-friendly solutions. However, policy instruments play a crucial role in shaping and guiding the development of smart eco-cities. Policy frameworks, regulations, and incentives create an enabling environment for implementing sustainable practices and innovative technologies and ensure the effective integration of smart solutions into urban environments. The effective use of policy instruments is essential in harnessing the full potential of technological advancements and addressing environmental challenges to realize the vision of smart eco-cities. Indeed, governance and smart eco-cities are deeply intertwined in a self-reinforcing relationship. To put it differently, governance is at the core of smart eco-cities ,These policy instruments create a supportive environment for the materialization of smart eco-cities. They provide guidance, incentives, and regulations that drive sustainable development, promote technological innovation, and enhance the overall quality of life for residents. As revealed by Joss and Cowley , based o5.2This subsection is concerned with the solutions, applications, and use cases in the realm of AI and AIoT technologies and innovations. In this context, a solution represents a comprehensive approach encompassing software, hardware, processes, and strategies to solve a particular problem. It is implemented in real-world use cases, which depict practical scenarios illustrating how data-driven technologies work in action. On the other hand, an application refers to specific software designed to perform tasks, often utilizing both AI and AIoT models and algorithms. While these terms can overlap, they offer distinct perspectives: applications focus on software functionality, solutions provide holistic problem-solving approaches, and use cases offer practical insights into real-world implementations, collectively advancing data-driven technologies and innovations.The use and application of AI and AIoT in environmental sustainability, climate change, and smart cities have dramatically increased from 2016 onward. This is seen as a sequel to the wide adoption of IoT and Big Data technologies and solutions in the various domains of smart cities for advancing environmental sustainability, as documented by many studies . As pre5.2.15.2.1.1•Water resources conservation: ML techniques, such as ANN, SVM, FL, ANFIS (an ANN-based on FL), LR, and Key-Nearest Neighbors (KNN), have been applied to predict stream flow and examine water quality parameters . So•Energy conservation and renewable energy: Energy-related studies have harnessed ML and DSS with techniques such as ANN, FL, SVM, DT, Evolution Strategies (ES), Evolutionary Computing (EC), BN , and ANFIS for tasks including energy operation, production, distribution, maintenance, and planning .•Sustainable transportation: ML has been applied to traffic forecasting using ANN, DT, and time series models ,172. Add•Biodiversity conservation and ecosystem services: ML techniques, including FL, GA, ARIES, and BN , have been used to address different aspects of biodiversity conservation and assess ecosystem services .The following empirical studies have significantly contributed to the advancement of emerging smarter eco-cities by applying AI and ML models and techniques to various aspects of environmental sustainability.The insights gained from the studies conducted on water resources conservation can inform effective water resources management strategies, aiding in the conservation and sustainable use of water in existing smart eco-cities. By utilizing AI algorithms, energy conservation and renewable energy studies have contributed to energy conservation efforts and the integration of renewable energy sources within existing smart eco-cities. As to sustainable transportation, related studies have provided valuable insights for better transportation planning and management, facilitating more efficient and sustainable mobility within existing smart eco-cities. The research on biodiversity conservation and ecosystem services has contributed to a deeper understanding of the relationships between biodiversity, ecosystem services, and the sustainability of existing smart eco-cities. The findings and insights from these studies can inform evidence-based decision-making and promote sustainable practices in smart eco-city development.•Data acquisition: IoT devices equipped with sensors and actuators collect real-time data on environmental parameters. This extensive data acquisition network forms the foundation for environmental monitoring.•Data transmission: IoT enables seamless data transmission to centralized platforms, such as cloud computing, ensuring that data from distributed sources is readily available for processing and analysis. Connectivity protocols ensure efficient and secure data transfer.•Big data handling: IoT generates vast amounts of data and AI algorithms, including ML and DL, process these data to identify patterns, anomalies, and trends, providing insights into environmental conditions.•Predictive analytics: AI-driven predictive models utilize historical and real-time IoT-driven data to forecast environmental changes, which enables proactive decision-making and timely responses.•Automation and control: IoT's integration with AI allows for autonomous control of systems and processes. For instance, smart grids can adjust energy distribution based on real-time demand and renewable energy availability, promoting energy efficiency and conservation.•Resource optimization: AI algorithms optimize resource allocation and utilization, ensuring minimal waste and maximum efficiency in such areas as energy consumption, water usage, and transportation.•Remote monitoring: IoT-enabled devices can be remotely controlled and monitored, reducing the need for physical interventions and minimizing human impact on sensitive ecosystems.IoT, as a fundamental component of AIoT, significantly contributes to the above areas of environmental sustainability by providing a robust technical framework for data collection, analysis, and decision-making.Overall, IoT's technical capabilities within the AIoT framework enable comprehensive data collection, analysis, and automation, fostering environmentally sustainable practices and informed decision-making across various domains.•Water resources conservation: Several studies have focused on water resources conservation by applying ML and DSS, employing techniques such as ANN, SVM, FL, GA, and ANFIS. Key studies in this area include those by Mehr et al. , Oyebode•Energy conservation and renewable energy: Efforts to conserve energy and promote renewable sources have been supported by ML techniques such as LR, ANN, SVM, NC, and EC. Researchers and scholars like Akhter et al. , Alsadi •Sustainable transportation: Related initiatives have been advanced by applying ML, CV, and DSS utilizing ANN, DT, NC, SVM, FL, and time series models. Key studies in this area include those by Jiang and Zhang  and Liya•Biodiversity and ecosystem services: Biodiversity-related studies have explored modeling competition and population dynamics, often employing ML techniques like cellular automata . AdditioSimilarly, a range of theoretical and literature review studies have made notable contributions in the context of emerging smarter eco-cities across various domains:The research on water resource conservation has provided insights into effective water resource management strategies. Regarding energy conservation and renewable energy, the studies in this area have contributed to optimizing energy operations, promoting energy efficiency, and fostering the adoption of renewable energy technologies. The research on sustainable transportation has provided insights into traffic management, transportation planning, and improving the overall efficiency and sustainability of transportation systems. Concerning the studies in biodiversity conservation they contribute to our understanding of biodiversity and the sustainable management of ecosystems. Overall, these theoretical and literature review studies collectively contribute to the advancement of smarter eco-cities, and their outcomes provide insights into the development of evidence-based approaches for building more sustainable and resilient smart eco-cities.5.2.1.2•Scenario analysis: Studies have employed various AI and ML models and techniques. Noteworthy works have performed scenario analysis based on ML, EC, and FL using ANN, BN, SVM, GA, and neuro-fuzzy ; analys•Disaster management and resilience: The domain of disaster resilience has witnessed significant AI-driven contributions in prediction and forecasting and resilient infrastructure and urban planning. The studies carried out by Cheng and Hoang , Choubin•Marine resources management: AI research has played a pivotal role in marine resources management, encompassing water pollution monitoring, pollutant tracing in water quality, pollution reduction and prevention strategies, acidification mitigation, and habitat and species protection. These endeavors have harnessed various AI models and techniques, as demonstrated by the works of Lu et al.  and WangIn empirical AI research on climate change, notable attention has been directed toward specific areas, including scenario analysis, marine resources management, and disaster management and resilience. This focus has yielded significant contributions to advancing smarter eco-cities:This body of research underscores the critical role of AI in addressing climate change challenges, highlighting its potential to inform smarter eco-city initiatives and promote sustainable urban development. Specifically, the research on scenario analysis has provided insights into climate change impacts, vulnerability, and adaptation strategies; the interdependencies and interactions between different aspects of sustainability; understanding and managing CO2 emissions; and enabling the development of strategies for reducing carbon footprints. Regarding natural disaster analysis, the key insights gained pertain to predicting and forecasting natural disasters, resilient infrastructure planning, and environmental hazard detection. Disaster resilience studies have enhanced disaster resilience, enabling effective preparedness, response, and recovery measures. The studies on marine resources management have provided new perspectives on tackling different relevant challenges and enhancing the sustainable management of marine resources. Overall, these empirical studies provide valuable insights and advancements in understanding climate change challenges in the context of emerging smarter eco-cities.5.2.1.3During the first period, AI research on environmental sustainability was expanding to include smart cities based on various AI models, including ML, DL, CV, NLP, and robotics . It als5.2.2During the second period, 2020–2023, the empirical, theoretical, and literature research on AI increased and expanded across environmental sustainability, climate change, and smart cities. This increase and expansion pertain to energy conservation and renewable energy, water resources conservation, sustainable transport and mobility, biodiversity conservation, pollution control, climate adaptation and mitigation, and disaster management and resilience. The rapidly growing body of research in environmental sustainability, climate change, and smart cities has made further valuable contributions and advancements in the context of emerging smarter eco-cities, enhancing and extending the knowledge gained for the strategies and solutions for addressing and overcoming environmental challenges.5.2.2.12 emissions reduction and air quality improvement. Worth pointing out is that, during 2020–2021, COVID-19-related publications received 20 % of all citations, and 98 of the 100 most-cited publications were associated with COVID-19 [•Energy conservation and air quality efforts encompass pollution reduction and prevention, pollution monitoring, pollutant filtering and capture, air quality prediction, and early hazard warning, as well as the promotion of clean and renewable energy sources  and env•Sustainable transportation initiatives include the evaluation of energy consumption in household transportation through ML models , energy •Clean water security and water resource conservation efforts involve various aspects such as water quality management, water supply quantity optimization, water control, water treatment, and sanitation •Biodiversity and conservation endeavors revolve around enhancing and protecting natural capital, preserving ecosystem health, safeguarding habitats, restoring ecosystems, maintaining forest landscape visual quality, protecting species, conserving biological diversity, preventing marine pollution, and ensuring the preservation of marine resources . AmWhile the main areas of AI and AIoT research in environmental sustainability continued to attract attention, academic interest slightly decreased compared to the previous period. This is likely attributed to COVID-19 taking priority in research during the second period, inducing scholars in smart cities and environmental sustainability to investigate the link between COVID-19 and COIn the pursuit of creating more intelligent and sustainable urban environments, a wealth of research has emerged to address the unique challenges emerging smarter eco-cities face. These studies, spanning diverse domains, have made further contributions to shaping the trajectory of these cities. The studies on energy conservation and air quality studies significantly enhance their environmental performance and efficiency. The research in sustainable transportation research plays a crucial role in promoting eco-friendly and efficient urban transport systems. Studies on clean water security and resource conservation are pivotal for ensuring sustainable water resource management. The research on biodiversity and conservation is instrumental in supporting the development of eco-friendly and sustainable environments. These advancements can reshape the landscape of urban development models beyond smarter eco-cities in response to the growing wave of urbanization, making cities more intelligent and environmentally conscious.5.2.2.2Many more studies were conducted during the second period, indicating increased scholarly interest in climate change and its relation to AI and AIoT. The digitalized transformation triggered by COVID-19 significantly contributed to climate actions ,159. Con2 emissions through high energy consumption. It emphasizes the potential of AI to mitigate climate change by integrating knowledge, design strategies, and innovative technologies. It further discusses AI applications in transportation, urban energy, water use, and waste management, showcasing their impact on reshaping urban planning and design. Overall, it demonstrates that implementing AI-driven solutions can improve the sustainability of future cities and contribute to climate change mitigation efforts. Ivanova, Ivanova, and Medarov [In the face of escalating environmental challenges, the exploration of innovative technologies has become paramount to addressing the urgent concerns of climate change. This narrative dives into the realm of climate change mitigation and adaptation, shedding light on the pivotal role that AI and AIoT play in reshaping sustainable urban development in this regard. Climate change has accelerated the need for proactive measures, particularly in urban areas where high energy consumption contributes significantly to GHG emissions. Extensive research conducted during the second period highlights the urgency of addressing climate change as cities contribute significantly to CO Medarov  acknowle•Large-scale urbanization impacts under climate change scenarios.•Eco-services and tradeoffs model valuation for ecosystem services quantification.•Water utilization management using and combining Blockchain and AI.•AI for disaster response, digital response, and disaster management.•IoT-Based smart tree management.•AI and ML for wildfire evacuations, wildfire prediction and prevention, wildfire susceptibility mapping, human-caused wildfire occurrence, risk-reduction strategies for floods and droughts, conservation planning under climate-changing patterns, and green-roof irrigation optimization.•ML for flood prediction and protection.•AI for improving resilience and preparedness against flood events impact.•ANN for drought tolerance determination.•Evolutionary Neural Network (ENN) for forecasting carbon emissions, energy demand, and wind generation.•ANFIS for modeling climate change impact on wind power resources.•DL for modeling sub-grid processes in climate models.•ML for water security improvement and water demand forecasting in cities.•ML for adaptation policy.•AI for sustainable development.Furthermore, in connection with the first period, one of the areas that received more focus in the theoretical and empirical research on AI applications for climate change is disaster resilience and management, including early warning systems, resilience and planning, and simulation and prediction Some of these themes are linked to smart cities and smart eco-cities for renewable energy, water, biodiversity, climate, planning, and policy within the framework of SDG 11: Sustainable Cities and Communities, SDG 9: Clean and Affordable Energy, and SDG 13: Climate Action. Moreover, AI can be a critical change agent because it enables climate change mitigation through carbon neutrality in energy production, distribution, transportation, buildings, construction, and others . Some stIn the context of climate change mitigation and adaptation, IoT as a core component of AIoT brings critical technical aspects to the forefront. As to data sensing and collection, IoT devices equipped with sensors monitor relevant environmental parameters. This continuous data stream forms the basis for understanding climate change patterns and trends. In terms of network connectivity, IoT ensures seamless data transmission and communication between devices and central data repositories. Robust communication protocols and networks, such as 5G/6G, facilitate rapid data sharing. Concerning data analysis, AI algorithms process vast datasets from IoT sensors, identifying climate change patterns and trends. ML/DL models and algorithms enable predictive analytics for anticipating changes and their potential impacts. IoT-connected weather and climate monitoring stations, coupled with AI-driven forecasting models, enable the development of early warning systems for extreme weather events and natural disasters. Moreover, IoT devices track energy consumption in real-time as part of climate change mitigation approaches. AI algorithms analyze these data to optimize energy use, identify areas for conservation, and promote the integration of renewable energy sources to reduce environmental impacts. Also, IoT-enabled infrastructure, such as smart buildings and resilient urban planning, enhances adaptation efforts. In this regard, sensors monitor structural integrity and climate-related risks, facilitating adaptive responses. Furthermore, IoT sensors in ecosystems track changes in flora and fauna behavior and health. AI algorithms aid in assessing the impact of climate change on biodiversity and guiding conservation efforts. Additionally, IoT-connected satellites and drones equipped with AI-enabled remote sensing technology provide critical data for monitoring various environmental changes. Lastly, regarding real-time decision support: IoT systems provide real-time climate data and actionable insights to decision-makers, allowing for adaptive strategies and informed policy development. In sum, IoT's technical capabilities within AIoT are instrumental in climate change mitigation and adaptation efforts. They enable data-driven decision-making, resource optimization, and resilience-building, all crucial components in addressing the challenges posed by climate change.5.2.2.3Compared to the first period, AI, AIoT, and Blockchain technologies have proliferated and expanded, attracting more research interest, especially with their applications in smart cities. This also implies that smart cities are embracing AI and AIoT solutions developed initially for the different areas of environmental sustainability and climate change as separate fields.•optimizing energy production and consumption via domotics (home automation),•predicting the risks of climate change via ML algorithms and climate models,•monitoring changes in the natural environment via remote sensing with autonomous drones, and•operationalizing transport systems via mobility-as-a-service (MaaS).AI and AIoT applications span many domains of smart cities in the field of environmental sustainability. In a systematic literature review on smart cities and AI, Yigitcanlar et al.  found thAI applications related to the latter theme also include the management of transport systems of cities in terms of shared autonomous mobility-on-demand , autonomBroadly, AIoT can be used in smart cities to analyze and track how different consumers and residents use energy to make decisions on where and what kind of renewable energy sources could be used, as well as where energy is being wasted and how it can be directed for other uses or conserved. Sleem and Elhenawy  discuss Furthermore, AI and AIoT have been instrumental in developing advanced waste collection systems that optimize several parameters and maximize efficiency. Fang et al.  provide In particular, the need to overcome the constraints and complexities associated with conventional approaches , especially the status and waste level in bins, has driven the development and implementation of various advanced techniques. These include PSO , ANN . These In more recent years, Blockchain has become an innovative technology and solution for smart cities in environmental sustainability. It has been used by many governments to improve environmental sustainability. Integrating blockchain into renewable energy sources can unlock energy sustainability by facilitating the development of a decentralized and democratized energy system while aiding in improved climate governance through its attributes of transparency, global decentralization, and collaborative capabilities . As demoThe value of Blockchain technology lies in storing data on green energy production activities related to environmental degradation and air pollution; enabling new means of green energy production, supply chain and logistics, real-time data collection and analysis for timely decision-making pertaining to green and low-carbon processes; and monitoring EV charging systems . MoreoveRegarding the role of AI in preventing and reducing marine pollution , BlockchOverall, Blockchain technology, combined with AI and IoT, is crucial in advancing environmental sustainability. It provides a decentralized and transparent platform for securely recording and verifying transactions, data, and information. Integrating AI and IoT enables efficient data collection, analysis, and decision-making processes, leading to improved resource management, reduced environmental impact, and enhanced sustainability practices. The combination of blockchain, AI, and IoT allows for the development of innovative solutions such as smart grids, decentralized energy systems, waste management, and carbon footprint tracking. By fostering trust, traceability, and accountability, blockchain enhances the implementation of sustainable practices and facilitates the transition toward a greener and more sustainable future.The potential benefits of smart cities in catalyzing the transition to SDG 11 through advanced technologies and data-driven approaches are evident. Iris-Panagiota and Egleton  explore 66.1AI, IoT, and Big Data technologies are transforming how smart cities — and hence smart eco-cities — function by optimizing their processes, enhancing their practices, augmenting their solution capabilities, and improving their environmental sustainability performance. Since the mid-2010s, the data-driven technologies and solutions of smart cities as changing elements have gradually impacted the dynamics of eco-cities toward becoming smart in their approach to environmental sustainability by integrating their core domains with smart city domains. This will continue in the same direction as the AI, IoT, and Big Data technologies and solutions of smart cities become more advanced and integrated with sustainable technologies and strategies to provide innovative approaches that can demonstrate the ability to tackle more complex challenges. This, in turn, means making smart eco-cities smarter in their pursuit of achieving environmental sustainability thanks to the increasing use of AI and AIoT applications. The essence of AIoT revolves around the need to harness and leverage the power of smart city technologies and solutions, given the clear synergies in their operation concerning the optimization, efficiency, management, and planning processes of smarter eco-cities. This entails integrating their systems, coordinating their domains, and coupling their networks, creating many new opportunities that could be realized in environmental sustainability.At the technical level, AI empowers the analysis of the colossal amounts of data generated via the IoT infrastructure in smart cities  and hencConcerning environmentally smart sustainable urbanism as an underlying paradigm of smart eco-cities, it is increasingly becoming a powerful societal framework for the transition toward environmental sustainability. This lies in developing joint actions for preserving the environment based on analyzing large-scale databases, understanding the complexity of climate change and modeling and simulating its potential impacts, improving the health of ecosystems, and enabling high integration of renewable energy and smart energy , enhanciHowever, the dynamics of smarter eco-cities should evaluate AI and AIoT technologies as key components initiating changes in different domains. Investments in large-scale AI and AIoT as digital ecosystems are expected to positively impact smarter eco-cities that may involve feedback mechanisms, resulting in further adoption of these ecosystems and additional future investments. In other words, AI and AIoT technologies are likely to benefit from providing innovative applications in response to the need to overcome environmental sustainability challenges. Accordingly, they may exhibit positive feedback in that the more their solutions are implemented, the more likely they will be further implemented, thanks to network effects, learning, adaptation, and coordination. While the relationship between outcomes, investments, and implementations is expected to advance the transition of smart eco-cities toward environmental sustainability, stating a strong causal relationship resulting from such linkages needs to be more accurate. For this reason, coupled with other complex intertwined internal and external factors, understanding the dynamics of smarter eco-cities remains a daunting and uncertain challenge. This can be justified and elucidated in what remains of this discussion.6.2AI, IoT, and Big Data technologies pose significant challenges when making smart eco-cities environmentally smarter. Therefore, paying attention to both the opportunities and threats of AI and AIoT technologies is necessary. Smarter eco-cities must be environmentally friendly, thereby minimizing the negative impacts resulting from the wide use and increasing adoption of the applied solutions of AI and AIoT technologies. These enabling, integrative, and constitute technologies are embedded into a much wider socio-technical landscape involving a complex set of intertwined and heterogenous factors and actors. There is a risk of a mismatch between the environmental goals of smarter eco-cities and the opportunities offered by AI and AIoT technologies. This is due to their indirect, direct, rebound, and systemic effects, which are generated through their development, design, use, application, and disposal  are considered black boxes, lacking transparency in decision-making. This lack of insight can hinder trust in and adoption of smart city systems, especially when critical decisions are at stake.•Bias and fairness: Bias in AI models, derived from biased training data, can perpetuate existing inequalities in resource allocation and exacerbate environmental disparities. Overcoming these biases and ensuring fair outcomes is a daunting task. Biased decision-making becomes evident in real-time and predictive analytics, hindering the pursuit of environmental sustainability.•The trade-off between accuracy and interpretability: Striking a balance between accurate predictions and clear explanations is essential, as more complex models might offer better predictions but sacrifice interpretability.•Interdisciplinary nature: Addressing environmental sustainability and climate change requires expertise from diverse fields. Ensuring that AI and AIoT solutions are interpretable to domain experts, policymakers, and citizens across various disciplines is a challenge, as technical terminology can create barriers to communication.•Data privacy and security: While explaining AI decisions and promoting transparency, care must be taken not to compromise sensitive or private information about individuals, potentially compromising their privacy.•Dynamic and evolving environments: Smart cities and smarter eco-cities are dynamically changing environments, necessitating adaptable and robust methods for interpreting AI decisions.•Education and adoption: Educating stakeholders, including policymakers, city planners, city managers, and citizens, about the benefits and limitations of AI and AIoT solutions, building trust and confidence, and encouraging adoption are critical factors in realizing smarter eco-cities.Explainable AI (XAI) and Interpretable ML (IML) encounter significant challenges in the context of AI and AIoT solutions for smart cities, environmental sustainability, and climate change, particularly in the evolving landscape of smarter eco-cities. XAI and IML are interconnected concepts aiming to enhance the transparency, comprehensibility, and credibility of AI models for various stakeholders involved in smarter eco-city development. While XAI focuses on explaining the decision-making process of AI systems, IML specifically concentrates on creating ML models that produce easily interpretable outcomes. XAI encompasses diverse approaches, including IML techniques, to explain AI decisions, aligning with the broader aim of enhancing explainability in AI. Both XAI and IML play pivotal roles in creating AI and AIoT systems that foster accountability, trustworthiness, and effective human-AI interaction, which is vital for making informed decisions in the context of smarter eco-cities. Nonetheless, several challenges and issues arise in this context , inThe real challenge of XAI lies in granting substantial power to smarter eco-city systems without simultaneously enabling them to explain the intricate decision-making processes to different groups of domain experts. AI and ML models and algorithms assume control over decision-making by analyzing generated data, predicting outcomes, and maximizing value based on certain criteria. This reduces the rich complexity of urban life and the unpredictability of urban dynamics and systems to narrow quantitative and unitary languages, potentially disregarding the significance of cultural, ethical, social, and political values. As a result, technological advancements may pose difficulties in achieving the status of smarter eco-cities due to the mechanistic way of perceiving these complex systems. Therefore, a recent wave of research has started to focus on XAI to address some of the concerns posed by the application of AI in various domains. Mayuri, Vasile, and Indranath  present XAI and IML methods are pivotal for the sustainable advancement of AI and AIoT solutions, allowing society to foster trust in the environmental and social-economic aspects of sustainability. These methods explain accuracy, fairness, transparency, accountability, and human-centeredness outcomes in AI and AIoT-powered decision-making, addressing ethical and governance concerns. These principles hold substantial relevance for data-driven decision-making in smarter eco-cities, thereby the need for creating explainable/interpretable models, techniques, and tools. Collaborative efforts among AI/AIoT experts, environmental scientists, urban planners, and policy-makers are essential to ensure the effective contribution of AI and AIoT technologies to environmental sustainability and climate change mitigation in the evolving landscape of smarter eco-cities. Also, future research endeavors will play a pivotal role in realizing transparent, effective, and ethically sound applications of XAI and IML methods within AI and AIoT solutions, advancing environmental sustainability in smarter eco-cities while ensuring equitable outcomes for all stakeholders.6.5AI technology's ethical and humanistic issues and risks are subject to long-standing intellectual and philosophical debates. The development, deployment, and adoption of AI technology raise these concerns, irrespective of the environmental benefits of its applied innovative solutions, depending on the application domain. Against the backdrop of this study, the use of AI involves making biased decisions, exacerbating privacy and cybersecurity, and limiting public trust ,293,294.The underlying ethical gaps of AI technology call for designing and implementing appropriate regulatory frameworks to address the counterproductive outcomes emanating from the penetrative patterns of AI The realization of the common good of AI and AIoT technologies remains highly improbable when AI systems operate solely according to the algorithms designed and implemented by powerful corporations driven by ambitions for power, profit, and extensive reach and influence. These tech giants pursue various trajectories and explore uncharted possibilities, raising concerns about the potential consequences of the large-scale implementation of AI and AIoT systems. There is an urgent demand for well-regulated and responsible AI and AIoT systems that prioritize safeguarding public and civic values within the context of smart eco-cities. Such systems must be designed to ensure that the broader benefit to society takes precedence over corporate interests and unchecked advancements. Indeed, civic values and public values play vital roles in the functioning of both civil society and government. These values are the moral compass that guides individuals, communities, and public institutions in their pursuit of a just, inclusive, and prosperous society. In a civil society, civic values, such as social justice, freedom, tolerance, compassion, and tolerance, are the cornerstones of a harmonious and fair community. They inspire individuals to engage in civic activities, advocate for their rights, and work collectively to address societal issues. Civic engagement is fostered by these values, encouraging citizens to participate in public discourse and actively contribute to the betterment of society. At the same time, public values are fundamental to the proper functioning of government. These principles, including accountability, transparency, integrity, inclusivity, public participation, and environmental stewardship, ensure that public institutions operate in the best interests of the people they serve. However, for example, in relation to environmental stewardship, the crucial aspects of environmental protection, justice, and preservation are often sidelined when unregulated economic interests drive urban development.While technological advancements, such as AI and AIoT systems, have the potential to enhance the efficiency and effectiveness of both civil society and government, it is essential to recognize that certain core functions should not be outsourced to these systems. The decision-making processes guided by civic and public values require the nuanced judgment and ethical considerations that only humans can provide. AI and AioT systems can be valuable tools, but they should support and complement the efforts of individuals and institutions rather than replace or overshadow the importance of these foundational values. The interplay between civic and public values and emerging technologies should be carefully managed to ensure that they continue to serve as the moral and ethical foundations of our society and government. However, Kassens-Noor and Hintze  argue th6.6•Search strategy: Despite efforts to conduct a thorough literature search, some relevant studies may have been missed. Limitations in database selection, search keywords, language restriction, and inclusion/exclusion criteria could influence the breadth and depth of the included studies for synthesis. Moreover, as the study relied mainly on peer-reviewed documents, there is a risk of excluding a large part of grey literature and stakeholder input and not gaining extensive insights on emerging smarter eco-cities. This area of research is still evolving, and most of its existing work drew mainly from the fields of environmental sustainability, climate change, and smart cities with respect to AI and AIoT technologies and solutions. Even these fields are associated with a paucity of knowledge and a sparsity of empirical evidence due to the burgeoning nature of these technologies and solutions.•Publication bias: The study relied on published literature, and there is a risk of publication bias, where studies with positive results are more likely to be published, while studies with negative findings may be overlooked. This can affect the comprehensiveness and representativeness of the systematic review. It is common for researchers and journals to preferentially publish studies based on the direction or significance of their findings. This can lead to an overrepresentation of studies with positive results, while studies with negative results may be less likely to get published and thus be included in the systematic review. In addition, there is a language bias in that studies published in English are more accessible and commonly included in systematic reviews, leading to the potential exclusion of valuable evidence. Especially, English language was one of the inclusion criteria applied in the study. Funding sources are another bias regarding the studies funded by industry having a higher likelihood of being published — if they produce favorable results.•Data extraction and synthesis pertain to the complexities and challenges of extracting data from selected studies and synthesizing their findings. One of the primary issues posed in the study was the heterogeneity across studies. This included variations in study designs  that complicated the synthesis of findings and reporting formats (differences in the presentation of results) that affected the extraction and synthesis process. These differences can make it difficult to directly compare and combine data from different studies, thus obtaining a comprehensive overview of the evidence and drawing robust conclusions from the systematic review.It is important to acknowledge the methodological limitations of this comprehensive systematic review to allow the readers to assess the reliability and validity of the findings and understand the potential implications for future research and practice. These limitations, which arose from the various aspects of the review process, include:7Smarter eco-city scholars, practitioners, and policymakers have a new opportunity to foster sustainable development practices based on a new paradigm of solution-thinking grounded in a deeper understanding of the interplay between techno-scientific and socio-political solutions. Developing this multi-faceted change process is one of the most critical challenges of sustainable urban development to achieve the status of smarter eco-cities. This emerging area of research is empirically under-researched, theoretically under-developed, and critically under-thought to allow for large-scale implementations. This means that a plethora of problems and questions need to be addressed and answered to guide the development of smarter eco-cities and, hence, large-scale AI and AIoT deployments for the common good. Some gaps in our knowledge of emerging smarter eco-cities follow from our results and discussion. These gaps span a broad set of topics that are significant to investigate or critically engage with and that can be approached from various perspectives in the form of suggestions for future research.•Identify key drivers: Delve into the multifaceted drivers that underpin the evolution of smart eco-cities, encompassing social, economic, institutional, and political factors beyond technological and environmental aspects.•Evaluate effectiveness: Prioritize assessing the real-world effectiveness and scalability of AI and AIoT applications in smarter eco-cities, determining their actual impact on achieving SDGs.•Explore long-term benefits: Probe the long-term benefits and opportunities offered by AI and AIoT technologies in fostering sustainability practices in smarter eco-cities. This includes exploring their potential for resource optimization, energy efficiency, waste reduction, and enhancing the quality of life for citizens.•Overcome barriers and risks: Confront challenges and mitigate risks associated with AI and AIoT implementation, such as privacy concerns, data security, and governance frameworks.•Promote responsible AI practices: Investigate guidelines and best practices for the responsible design, deployment, and governance of AI and AIoT solutions. Ensuring fairness, transparency, and accountability in decision-making processes is essential.•Integrate disciplines: Foster interdisciplinary research merging environmental sustainability, climate change, and smart cities with AI and AIoT. This approach unravels intricate relationships and facilitates a comprehensive understanding of the complex interactions and interdependencies between these domains, and enables the development of integrated and holistic solutions•Formulate policies and frameworks: Develop robust policy and governance frameworks that facilitate the ethical and transparent use of AI and AIoT technologies and support their adoption and implementation in sustainable urban development. This includes examining regulatory mechanisms, standards, and guidelines to ensure transparency, accountability, and ethical use of these technologies.•Advance XAI and IML methods: Develop solutions for interpretability in intricate models, user-centric model training, tailored solutions for smarter eco-cities, resilience and adaptability enhancement, and ethical implications. This entails developing XAI techniques for elucidating complex AI models in AIoT systems, exploring IML integration to engage users in refining models, utilizing XAI and IML to tackle distinct environmental challenges, fortifying AIoT systems' resilience against dynamic urban scenarios, and scrutinizing the ethical and societal implications of deploying XAI and IML in AIoT solutions to ensure equity and transparency.•Promote community engagement: Explore ways to involve local communities, stakeholders, and citizens in the design, implementation, and monitoring of AI and AIoT solutions for smarter eco-cities. Their active participation can lead to more inclusive and effective outcomes.•Quantify environmental impact: Develop methodologies to quantify the environmental impact of AI and AIoT solutions in smarter eco-cities. This involves assessing factors like energy consumption, carbon footprint, and resource utilization to understand the overall sustainability gains.•Conduct lifecycle analysis: Assess the sustainability of AI and AIoT technologies across their entire lifecycle, from production to disposal. This holistic approach can reveal potential environmental hotspots and guide improvements.•Promote citizen participation: Explore ways to empower citizens with AI-augmented information and tools that enable them to participate in environmental conservation and sustainable behaviors actively.•Enhance behavioral insights: Investigate how AI can leverage behavioral insights to encourage environmentally friendly behaviors among citizens, such as energy conservation and waste reduction.•Perform case studies: Conduct in-depth empirical inquiries and practical applications of AI and AIoT in actual smart eco-city projects. These real-world insights illuminate challenges, best practices, and valuable lessons for effective integration.Considering the transformative potential of AI and AIoT technologies in reshaping smarter eco-cities, a comprehensive investigation is warranted to unravel the dimensions, opportunities, benefits, and challenges inherent in this emerging urban paradigm. Given the nascent stage of research at the intersection of environmental sustainability, climate change, and smart cities within the context of AI and AIoT solutions, the following avenues are crucial:By addressing these knowledge gaps and pursuing these research avenues, the field of smarter eco-cities can advance its understanding, implementation, and impact. This will contribute to developing more sustainable and technologically advanced urban environments that benefit society and the environment.8As disruptive technologies, AI and AIoT lay the foundational technological infrastructure essential for constructing the digital ecosystem of emerging smarter eco-cities to amplify and sustain their contributions to environmental sustainability goals. This pursuit involves enhancing the efficiency and effectiveness of their operations, functions, strategies, and policies in alignment with the environmental targets of SDG 11. Within this context, it is important to acknowledge the immense potential of AI and AIoT technologies to develop robust intelligent systems generating profound insights for decision-making processes. However, these technologies cannot serve as a universal remedy or panacea for the wicked problems characterizing smarter eco-cities as complex systems. In this study, we aimed to provide a comprehensive systematic review of emerging smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability, employing a unified approach to evidence synthesis. The study's key findings concerning the five research questions are outlined as follows:Interlinked foundational underpinnings of smarter eco-cities: The study showed that the fundamental concepts underpinning smarter eco-cities are intricately interconnected and build one on another on various scales. The key underlying urbanism paradigms, namely smart cities and eco-cities, serve as the foundation for integrating data-driven technologies and environmental solutions. Data-driven technologies enable real-time monitoring, analysis, and decision-making, while environmental solutions focus on optimizing resource efficiency and minimizing ecological footprint. Data-driven insights enhance the effectiveness of environmental strategies, ultimately contributing to creating more resilient, livable, and environmentally friendly urban environments.The materialization of smarter eco-cities: The study identified several intertwined factors contributing to the materialization of smarter eco-cities as an emerging paradigm of urbanism, including the growing need for sustainable development, advancements in technology, environmental considerations, policy instruments, and government initiatives, and the recognition of the potential of data-driven technologies in addressing complex environmental challenges.The primary AI and AIoT solutions harnessed in the development of emerging smarter eco-cities: The study identified many applied solutions of AI and AIoT technologies, demonstrating their role in urban planning, management, and development. These solutions encompass energy conservation and renewable energy, sustainable transportation management, traffic control, water resources conservation, waste management for efficient resource utilization, biodiversity and ecosystem services, environmental monitoring and control, climate change adaptation and mitigation, and disaster resilience and management.The benefits and opportunities of AI and AIoT technologies in fostering sustainability practices in emerging smarter eco-cities: The study identified the opportunities and benefits offered by AI and AIoT technologies in the context of environmental sustainability. Combined, these opportunities and benefits included optimized resource management, increased energy efficiency, enhanced waste management, improved transportation, and mobility management, reduced environmental impacts, increased resilience to environmental challenges, enhanced decision-making in urban management and planning, and the potential for creating more sustainable and technologically advanced urban environments. Identifying opportunities — favorable circumstances and possibilities — helps understand the potential areas where AI and AIoT solutions can bring about positive changes and contribute to the overall development of smarter eco-cities. Benefits — positive outcomes and advantages — highlight the tangible and intangible gains that can be achieved by adopting AI and AIoT solutions.Challenges and barriers arising in the implementation of AI and AIoT solutions for the development of emerging smarter eco-cities: The study identified and evaluated the key challenges pertaining to environmental costs, privacy concerns related to data collection and usage, cybersecurity risks related to interconnected systems, public trust, and social acceptance, limited technical expertise and knowledge, the lack of robust regulatory frameworks to ensure ethical and responsible AI and AIoT deployment and the requirement for addressing the social issues to ensure equitable and transparent use of AI and AIoT technologies.Overall, the study highlighted the significance of AI and AIoT technologies in advancing the transition toward environmental sustainability in smarter eco-cities. While these technologies provide new and largely expanded opportunities to understand better and prevent environmental problems, they pose significant challenges that must be addressed and overcome to successfully implement smarter eco-cities. Therefore, it is important to emphasize the need for interdisciplinary research, policy support, and collaboration among different stakeholders to overcome these challenges and maximize the benefits of these technologies.The synthesized evidence presented in this study has significant implications for researchers, practitioners, and policymakers involved in designing, managing, and planning smarter eco-cities. It offers valuable insights into the various dimensions of emerging smarter eco-cities and identifies best practices that can inform decision-making processes. The systematic review serves as a knowledge repository, guiding stakeholders in understanding the current state of research, identifying gaps, and shaping future strategies for sustainable urban development. Firstly, by identifying the core conceptual underpinnings of emerging smarter eco-cities and the intricate interconnections between them (RQ1), coupled with the intertwined factors propelling the materialization of smarter eco-cities (RQ2), the study encourages interdisciplinary collaboration among researchers and practitioners from various fields and foster new research avenues and practice pathways, leading to a more thorough understanding and focused improvement of emerging smarter eco-cities. Secondly, by identifying the key applied solutions of AI and AIoT technologies (RQ3), the study can inform researchers, practitioners, and policymakers about the technological advancements and innovative approaches employed in fostering sustainable urban development practices. Furthermore, exploring the potential opportunities and benefits offered by AI and AIoT technologies in this regard (RQ4) can provide valuable insights for decision-makers and urban planners seeking to leverage these technologies for achieving the SDGs, especially SDG 11. Lastly, identifying challenges and barriers in implementing AI and AIoT solutions in emerging smarter eco-cities (RQ5) can inform policymakers and stakeholders about the potential obstacles and open issues that need to be addressed when integrating these technologies into urban development strategies. Overall, the research, practice, and policymaking implications of this study encompass a wide range of areas, including urban planning, technology implementation, sustainability practices, and policy development, facilitating informed decision-making, and promoting the advancement of smarter eco-cities.Ultimately, the findings of the systematic review contribute to the broader goal of creating smarter eco-cities that prioritize environmental sustainability, resource efficiency, and human well-being. The invaluable insights gained accordingly will empower stakeholders to make strategic choices, implement innovative solutions, and drive positive change in urban planning and management. By leveraging the potential of AI and AIoT, policymakers, urban planners, researchers, and practitioners can work together toward creating smarter, more resilient, more livable, and environmentally conscious cities that meet the needs of present and future generations. To sum up, AI and AIoT technologies will offer unprecedented capabilities to rise to many of the grand environmental challenges, but how these technologies will be used and what other possible directions this use might take is up to all of us, especially the research community, and for the time to tell.Simon Elias Bibri: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Visualization, Software, Writing - Original Draft, Writing - Review & Editing. John Krogstie: Conceptualization, Writing - Review & Editing. Alexandre Alahi and Amin Kaboli: Writing - Review & Editing. All authors read and approved the published version of the manuscript.The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this article.