The increasing frequency and intensity of natural and man-made disasters have highlighted the necessity for intelligent disaster management systems capable of providing rapid response and accurate situational awareness. Conventional disaster management approaches often rely on manual observations, fragmented communication infrastructures, and delayed reporting mechanisms, which can significantly reduce the effectiveness of emergency response operations. The emergence of the Internet of Things (IoT) has introduced new opportunities for real-time monitoring, data acquisition, predictive analytics, and automated decision-making. This paper presents an IoT-enabled disaster management system that integrates distributed sensors, wireless communication networks, cloud computing platforms, and machine learning techniques to improve disaster preparedness, detection, response, and recovery. The proposed framework continuously monitors environmental and structural parameters, analyzes collected information through intelligent algorithms, and generates early warnings for emergency authorities and affected communities. The system aims to minimize casualties, reduce property damage, and enhance coordination among disaster response agencies. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced operational efficiency when compared with conventional disaster management systems. The proposed solution offers a scalable, reliable, and cost-effective approach for building resilient smart cities and disaster-resistant communities.

Keywords— Internet of Things, Disaster Management, Smart Cities, Early Warning Systems, Machine Learning, Cloud Computing, Emergency Response.