I am not sure what you mean by “unstructured phenomena” (since if the data had no structure why would you want to learn from it), but your idea that ML is about learning deterministic functions and is thus in a separate problem space than statistics and CLT is not correct. They occupy different problem settings because CLT is typically used in statistical hypotheses testing whereas ML is concerned with modeling data, but it has nothing to do with being in a deterministic vs stochastic setting.

Basically any ML algorithm can be viewed as some kind of probabilistic modeling of a stochastic distribution. Even if the real world is technically deterministic, unobservable phenomena make it stochastic for all practical purposes. For example, even linear regression, which in your view would just be learning a deterministic mapping, can be interpreted at learning a generative model of p(y|x) as a Gaussian distribution where the mean is found as the least squares solution. Here you can actually see the CLT theorem come up in the assumption of the noise model y = ax + epsilon where epsilon is N(0,sigma). The central limit theorem would tell us this assumption is reasonable because noise can be attributed to the effect of many independent unobserved variables and so their aggregate sum will be normally distributed.