This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. The paper then delves into the concept of resilience and the role of microgrids in maintaining power stability. It reviews various AI techniques and methods and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control. The paper discusses the increasing reliance on renewable energy sources in the European Union and the challenges posed by natural and human-induced threats to power systems. It highlights the importance of resilience in power systems, particularly in the face of extreme weather events and other disruptions. The paper reviews various AI techniques, including machine learning, probabilistic learning, statistical methods, search and optimization techniques, and game theory, and their applications in enhancing the resilience of microgrids. It also discusses the challenges of applying AI to microgrid resilience, including the limitations of current AI applications and the need for further research. The paper concludes with a summary of the work and outlines the path forward for future research in this field.This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. The paper then delves into the concept of resilience and the role of microgrids in maintaining power stability. It reviews various AI techniques and methods and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control. The paper discusses the increasing reliance on renewable energy sources in the European Union and the challenges posed by natural and human-induced threats to power systems. It highlights the importance of resilience in power systems, particularly in the face of extreme weather events and other disruptions. The paper reviews various AI techniques, including machine learning, probabilistic learning, statistical methods, search and optimization techniques, and game theory, and their applications in enhancing the resilience of microgrids. It also discusses the challenges of applying AI to microgrid resilience, including the limitations of current AI applications and the need for further research. The paper concludes with a summary of the work and outlines the path forward for future research in this field.