One revolutionary step in redefining disaster response procedures is the use of agentic AI in crisis management. Conventional methods of disaster management mostly depend on human judgement, which is frequently sluggish, prone to mistakes, and overpowered by the complexity of ever-changing emergency situations. A new paradigm for handling such difficulties is provided by agentic AI, which is distinguished by its capacity for autonomous decision-making, adaptive learning, and real-time data processing. This paper examines how agentic AI can be incorporated into disaster response systems, emphasising how it can automate crucial decision-making, maximise resource allocation, and offer real-time insights in emergency scenarios. We explore the underlying technologies, including natural language processing (NLP), machine learning, and multi-agent systems, and show how they can be used to improve situational awareness, coordination, and the precision of decisions. We offer experimental data demonstrating the effectiveness of Agentic AI in enhancing resource distribution efficiency and disaster response times using mathematical modelling. Furthermore, we provide case studies from both man-made and natural disasters to highlight the practical benefits and difficulties of implementing such systems. We describe the possible development of AI-driven crisis management systems by talking about prospective trends, touching on scalability and ethical issues. With insights into its real-world uses and future potential to provide more robust, efficient, and effective disaster response frameworks, this chapter provides a thorough knowledge of how agentic AI might reinvent crisis management.