Abstract: Lately, distribution systems have grown increasingly intricate and vulnerable to a wide range of disruptions, such as natural disasters, cyberattacks, and equipment failures. As a result, there is a growing need for methods that can improve the resilience of these systems and minimize their downtime. In the realm of cutting-edge technology, novel methods of artificial intelligence are coming to the forefront, with machine learning (ML) leading the pack and becoming increasingly applied in many sectors including the energy sector. These automated techniques can help enhance the resilience of distribution systems by providing real-time data analysis, predictive modeling, and automated decision-making capabilities. Accordingly, this chapter delves into the role played by different ML techniques in Revitalizing the tenacity of distribution networks. Specifically, it provides a comprehensive review of the existing studies on the application of ML in distribution systems’ resilience and provides several case studies to illustrate the practical applications of these robust methods aimed at minimizing the frequency of disruptions from both natural and man-made disasters. Additionally, this chapter details the challenges of deploying ML techniques for distribution systems resilience along with highlighting the future directions of research in this area that will address the challenges to fully leverage the potential of AI-powered approaches for improving distribution system resilience. This chapter will act as an insightful resource for different key stakeholders, researchers, and students with a vested interest in this area.