This chapter explores the transformative potential of Artificial Intelligence (AI) in predicting and managing natural disasters, focusing on earthquakes, floods, hurricanes, typhoons, and volcanic eruptions. The chapter reviews various AI techniques employed in disaster management, including machine learning models for forecasting, pattern recognition and anomaly detection, integration of AI with existing monitoring systems, and predictive models for aftershocks. It emphasizes the role of AI in enhancing traditional disaster management practices by improving the accuracy, timeliness, and efficiency of prediction, monitoring, and response efforts. The chapter discusses the integration of AI with remote sensing technologies, highlighting its effectiveness in analyzing satellite imagery and aerial photography for rapid damage assessment, flood mapping, and storm tracking. Real-time monitoring and early warning systems powered by AI are examined, emphasizing their potential to mitigate the impact of flood events and volcanic eruptions by providing timely alerts based on integrated data from diverse sources. Challenges and future directions for AI in natural disaster management are also addressed, including data quality and availability, model interpretability, and the need for localized calibration of AI models to account for regional variations. Future research directions are highlighted, focusing on the integration of AI with emerging technologies like the Internet of Things (IoT), leveraging advanced data analytics, fostering global collaboration and knowledge sharing, and establishing public–private partnerships to drive innovation in AI solutions for disaster management.