ML is a system that takes a large data set, and an error function, and finds a generated output that minimizes the loss. What data set and error function are you proposing for "RNG"?
That's a wrong simplification of ML. Take RL for instance.
And the parent already explained a concept: generating maps. It could still have an RNG as the base (noise function over something), but then use ML to place elements based on existing human-made maps.
