  In recent years, machine learning (ML) techniques have emerged as powerful
tools for studying many-body complex systems, and encompassing phase
transitions in various domains of physics. This mini review provides a concise
yet comprehensive examination of the advancements achieved in applying ML to
investigate phase transitions, with a primary focus on those involved in
nuclear matter studies.
