This work focuses on the application of diffusion models, a very well-known type of generative artificial intelligence, to the mathematical and physical problem of the computation of entropy. The main goal of this work is to show that diffusion models can be used to compute entropy in a very efficient way, exploiting their recognized ability to learn the underlying data distrbution of a system. To this scope, first of all a good part of the presented work has been dedicated to the study of similar methods present in literature, to understand in particular which datasets are used to benchmark the task of entropy computation.