  We give an overview of recent developments in the modeling of radiowave
propagation, based on machine learning algorithms. We identify the input and
output specification and the architecture of the model as the main challenges
associated with machine learning-driven propagation models. Relevant papers are
discussed and categorized based on their approach to each of these challenges.
Emphasis is given on presenting the prospects and open problems in this
promising and rapidly evolving area.
