Machine Learning (ML) technology has become an essential tool in natural disaster management, offering predictive capabilities and data-driven decision-making to minimize the impact of catastrophic events. Disaster management is a critical field that involves preparedness, response, mitigation, and recovery from natural and man-made disasters. With the increasing availability of data and computational power, machine learning (ML) has emerged as a transformative technology in enhancing disaster management efforts. ML techniques, including deep learning, reinforcement learning, and predictive analytics, are widely applied in various phases of disaster management. These applications include early warning systems, real-time disaster monitoring, damage assessment, resource allocation, and postdisaster recovery. Key technologies such as remote sensing, geographic information systems (GIS), Internet of Things (IoT), and big data analytics play a crucial role in collecting and analyzing disaster-related data. ML models can process satellite imagery, sensor data, and social media feeds to predict disasters like earthquakes, floods, wildfires, and hurricanes with greater accuracy. Additionally, ML-based decision support systems improve emergency response by optimizing evacuation routes and resource distribution. Despite its significant potential, challenges such as data availability, model interpretability, and ethical considerations remain. Addressing these challenges through interdisciplinary collaboration and advanced AI research will further enhance ML’s role in disaster management. This paper explores the latest advancements, key applications, and future prospects of ML in disaster management, highlighting its impact on minimizing loss and improving disaster resilience.