  In the rapidly advancing landscape of contemporary technology, power
electronics assume a pivotal role across diverse applications, ranging from
renewable energy systems to electric vehicles and consumer electronics. The
efficacy and precision of these power electronics systems stand as cornerstones
of their functionality. Within this context, the integration of machine
learning techniques assumes paramount significance. This article endeavors to
present an extensive and comprehensive review of the machine learning
techniques that find application in power electronics control and optimization.
Through meticulous exploration, we aim to elucidate the profound potential of
these methods in shaping the future of power electronics control and
optimization.
