This chapter explores machine learning based fault detection strategies for three-level Neutral Point Clamped (NPC) inverters, which are widely used in power electronics due to their reduced harmonic distortion and lower power losses. Despite these advantages, NPC inverters remain vulnerable to faults—particularly open-circuit faults, which are more challenging to detect than short-circuit faults. To address this, this chapter focuses on leveraging ML algorithms to classify and detect faults using historical and real-time system data. Key ML techniques discussed include artificial neural networks (ANNs), and ensemble machine learning (EML) methods. EML and ANNs are highlighted for their robustness and accuracy in identifying various fault types. The chapter also emphasizes the role of explainable ML to ensure transparency and trust in fault detection systems for industrial applications. Furthermore, the implementation of ML-based detection on digital signal processors (DSPs) is examined, demonstrating the practical feasibility of real-time applications. Overall, the chapter highlights how ML enhances fault resilience and operational reliability in power electronic systems.