The growing impact of natural disasters, driven by climate change and urbanization, underscores the need for advanced tools in disaster risk management. Geospatial technologies and AI offer unparalleled opportunities to enhance disaster risk management by improving hazard detection, vulnerability assessment, and real-time monitoring. This chapter explores the role of geospatial technologies and artificial intelligence (AI) in enhancing hazard mapping, vulnerability assessment, and real-time monitoring. Geographic Information Systems (GIS) and remote sensing provide critical tools for identifying hazard-prone areas and vulnerable communities, guiding disaster preparedness and resource allocation. AI enhances these efforts by analysing vast datasets to improve hazard predictions and vulnerability assessments, supporting early warning systems that can save lives. Machine learning algorithms, combined with geospatial data, offer precise risk assessments and predictions, helping authorities make informed decisions. In real-time monitoring, satellite imagery, drones, and IoT sensors provide continuous updates on disaster events, while AI-driven image analysis accelerates damage detection and response efforts. This chapter highlights key case studies, such as wildfire monitoring and high-risk area forecast in central India and flood management in Northeast India, demonstrating the efficacy of these technologies. Integrating geospatial technologies and AI into disaster risk frameworks is vital for building resilient, disaster-ready communities. While promising, challenges like data accessibility, availability, infrastructure, and ethical concerns remain.