This chapter builds on the previous discussion of implementing ML on DSPs for power electronics and motor drives, addressing key limitations such as computational constraints, data scarcity, and the complexity of deploying deep learning in real-time systems. To overcome these challenges, it explores emerging AI techniques like Physics-Informed Neural Networks (PINNs) and lightweight Convolutional Neural Networks (Light CNNs), which offer improved efficiency and feasibility for embedded applications. The chapter also addresses strategies to manage data limitations in ML-based power electronics, emphasizing the importance of robustness and uncertainty quantification for reliable real-time operation. It highlights methods for improving explainability in ML models to enhance trust and interpretability, crucial for industrial adoption. Experimental case studies are presented, showcasing real-time ML implementations across diverse embedded platforms, including FPGAs, DSPs, and hybrid systems. These implementations are critically analyzed to evaluate performance, feasibility, and practical deployment outcomes. Overall, the chapter identifies the most promising AI-driven approaches for enhancing real-time fault detection and control in power electronics and motor drive systems. It concludes by outlining future research directions focused on improving model efficiency, scalability, and trustworthiness, paving the way for next-generation intelligent control systems in industrial applications.