As power electronic converters process an ever-growing share of global electricity, optimizing their efficiency and reliability has become paramount for secure and sustainable energy systems. These converters are subject to rigorous operational and environmental stresses that can induce complex failure modes. While predicting these failures is challenging, it is essential for preventing catastrophic system downtime. Consequently, there is a critical need for robust, cost-effective condition and health monitoring solutions that enable predictive maintenance and life-cycle cost reduction. At the intersection of deep electrification and digitalization, Artificial Intelligence (AI) is emerging as a transformative tool for solving optimization, regression, and classification problems. This lecture explores the application of AI for the monitoring of key power electronic components, such as semiconductor devices and capacitors, as well as full converter systems. Through selected case studies, the lecture will demonstrate how AI addresses existing research gaps and enhances the performance and cognitive capabilities of modern power electronic systems.