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?

As others have stated, it is the sample mean, under rather general conditions, that converges to a normal distribution. Specifically, if you are drawing iid samples from a population with mean $\mu$ and variance $\sigma^2$ then
$$\frac{\sum_1^n \left(X_1-\mu\right)}{\sigma \sqrt{n}} \xrightarrow{d} N(0,1)$$
You'll notice that the denominator grows exactly as fast as the standard deviation of the numerator, so it doesn't converge (in probability), but if you draw $M$ samples of size $n$ and calculate the following statistic for each of the $M$ samples:
$$Z_n=\frac{\sum_1^n \left(X_1-\mu\right)}{\sigma \sqrt{n}}$$
You'll see that the empirical distribution (ECDF) will get closer to $\Phi(z)$ the larger $n$ gets, where we can define "closer" using the Kolmorogov-Smrinov distance (d_{KS}) to the standard normal distribution:
$$d_{KS} := \sup_{z} |\Phi(z) - ECDF(z)|$$
Why does this matter? When we are trying to estimate some aspect of a population, perhaps a parameter from a model, it's often the case that you define an estimator (e.g., like our $Z_n$) that converges to a normal distribution as $n\to \infty$ so you have a gauge on how accurate an estimate is likely to be (on average).
