Machine learning (ML) approaches may improve real-time decision-making and crisis management in emergency response planning. ML might enhance situational awareness, resource allocation, and crisis prediction to better respond to emergencies. Secondary data examines the literature on ML applications in crisis management, including predictive modeling, classification, reinforcement learning, and Clustering. ML can increase response efficiency by integrating varied real-time data sources, anticipating crisis evolution, and dynamically assigning resources. However, data quality, model interpretability, and privacy issues exist. The paper recommends explainable AI models and privacy-preserving technology to overcome these challenges. Policy implications include standardizing data standards, increasing ML model openness, and implementing ethical data use rules. By solving these difficulties, ML may be used to develop robust, adaptive, and ethical emergency response systems that save lives and improve crisis management.
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