The current investigations present the numerical investigations of an infection-based fractional order nonlinear prey-predator system using the stochastic procedures of the scaled conjugate gradient and the artificial neuron networks. The stochastic computational procedure SCGNNs is applied to solve three different cases using different fractional-order values. The **data** proportions applied 75%, 10, and 15% for training, validation, and testing to solve the infection FONPPS. Ten numbers of neurons have been used to solve the nonlinear biologicalbased differential model. The numerical simulations of the infectious disease FONPPS are accomplished using the SCGNNs, while the competitive performances have been presented using the Adams-Bashforth-Moulton approach. The numerical results of the nonlinear fractional-order biological system are calculated using the computational SCGNNs to reduce the mean square error. To ratify the exactness, reliability, capability, and aptitude of the proposed SCGNNs, the numerical measures are plotted using the regression, MSE, STs, correlation, and Ehs. The identical performances designate the precision and accuracy of the proposed stochastic scheme and the AE values found in suitable ranges based on the nonlinear fractional-order biological system. The AE values, and the plots of other performances represent the dependability and consistency of the proposed approach. In future studies, the stochastic SCGNNs are pragmatic to achieve the results of the lonngren-wave systems and fractional order nonlinear systems [37][38][39][40][41][42][43][44][45].    