Sample means and confidence intervals For a population, where its parameters are unknown, why is it that if we take samples, those samples create a distribution that resembles that of a Normal Distribution? What if the original population distribution is not normal, why don't the samples create a distribution that is similar to the population, because they should have similar features to the population?

It is not the case that samples from an arbitrary distribution are normal.
What you are thinking of is the Central Limit Theorem (CLT) which instead tells us the following...
Let $F$ be a (sufficiently regular) distribution with mean $\mu$ and standard deviation $\sigma$. Let $X_{1},X_{2},\ldots$ be a sequence of IID samples from $F$.
Then, the sample mean is "roughly" normal when a "large" number of samples $n$ are used:
$$
\frac{X_{1}+\cdots+X_{n}}{n}\approx N(\mu,\sigma^{2}/n).
$$
Note, in particular, that the uncertainty in our estimate of the true mean by the sample mean tends to zero as more and more samples are used (i.e., $n \rightarrow \infty$).
The above is not a rigorous statement ($\approx$).
More rigorous forms of the CLT involving different flavors of convergence can be found on the corresponding Wikipedia article.
I recommend you visit these in the future after learning about convergence of random variables.
