International Journal of Scientific Research and Engineering Development

Natural disasters such as floods, heatwaves, cyclones, and droughts continue to pose serious threats to human life and infrastructure. This paper presents an AI Disaster Prediction Web Application that uses machine learning algorithms and real-time meteorological data to predict potential disaster conditions and provide early alerts. The system collects environmental parameters including temperature, humidity, rainfall, wind speed, and atmospheric pressure from live weather APIs. A Random Forest machine learning model processes these inputs to classify possible disaster types and estimate their probability. The application includes an interactive web dashboard for real-time visualization, alert notifications, and precautionary recommendations. Experimental analysis demonstrates that the system achieves high prediction accuracy while maintaining low response latency. The proposed solution offers an intelligent, scalable, and userfriendly platform for disaster preparedness and early warning management. Keywords: Artificial Intelligence, Disaster Prediction, Machine Learning, Random Forest, Weather Analytics, Real-Time Alerts, Web Dashboard.

Siddhesh Mangalpalli, Muzammil Adam, Vinay Tatarshi, Shriniwas Sutar, Aishwarya Hosale,"AI Based Disaster Prediction" International Journal of Scientific Research and Engineering Development, V9(3): Page(458-461) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.