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Natural disasters with tremendous impacts on human lives, infrastructure, and ecosystems are frequent all over the world, which calls for intelligent, data-driven decision support systems for early diagnosis and effective crisis management. This paper demonstrates a Natural Disaster Diagnosis and Crisis Management System design and development featuring real-time environmental sensing, technological monitoring, and pre-emptive response planning within a unified decision-support framework. The proposed system include predict disasters using machine learning. It follows a modular architecture that integrates analytical risk assessment, formulation of preventive strategy, and dynamic action planning through an interactive GUI. NDDCMS structures disaster management into five operational phases, namely disaster diagnosis, early warning indicators, response during the disaster, post-disaster recovery, and preventive planning. Each phase integrates stepwise risk indicators and decision inputs to support officials and community response teams. It relies on quantifiable environmental parameters (e.g., precipitation, soil moisture, wind speed, and temperature fluctuation) and corresponding technological sensing mechanisms for the classification of risk levels and the improvement of early warning reliability. By focusing on a user-centered design and data-driven workflow, the system advances situational awareness, hastens decision-making, and closes the gap between disaster prediction and effective response. This framework enhances national and local-level disaster resilience by supporting viable risk reduction and crisis management strategies.