This chapter introduces the field of power electronics and explores the growing role of machine learning (ML) methods in this area. Power electronics focuses on the efficient conversion and control of electrical energy through semiconductor devices, playing a critical role in a wide range of applications, including industrial automation, renewable energy systems, and electric drives. As power converter systems become increasingly complex and dynamic, conventional control and design methods often struggle to manage their nonlinear and time-varying behaviors. In response to these challenges, ML has gained significant traction for its ability to model and adapt to complex system dynamics using data-driven approaches. Recent advancements in ML algorithms and computational power have enabled more effective handling of the intrinsic complexity of power electronics. This chapter highlights three core areas where ML integration has shown considerable impact: design optimization involves selecting topologies, sizing components, and synthesizing circuits more efficiently. Intelligent control enables adaptive and robust real-time operation while predictive maintenance ensures system reliability by supporting condition monitoring, fault diagnosis, and remaining useful life prediction.