In today’s era, the climate patterns have become unpredictable and easily variable as climate change is becoming a growing concern recently, it has become necessary to be prepared about the adverse effects of changing weather not only for our essential activities like agriculture etc. But also for disaster management as well. Forecasting of such changing patterns are always a better option to follow but traditional forecasting systems often comes with limitations like limited localization, inaccurate sensor readings and lack of dynamic alert systems. This paper presents an Intelligent Context-Aware IoT Weather Forecasting and Emergency Alert System that uses IoT supported weather monitoring, ensembled Machine learning, adaptive sensor calibration and dynamic alert severity system. The proposed system uses IoT framework to collect localized real time environmental data and enhances data reliability through filtering techniques. An ensemble ML framework combining Random forest, XGBoost, and Gradient Boosting models is used to improve prediction accuracy. In addition, a context aware alert mechanism dynamically varies warning severity based on Risk levels and estimated population exposure density. The system is implemented using ESP32, LoRa, and a Flutter based mobile application for real time access and monitoring with intelligent notifications. Experimental results demonstrate improved forecasting performance and more effective emergency alert prioritization compared to traditional single-model weather monitoring systems.

Keywords— Internet of Things (IoT); Weather Forecasting; Ensemble Machine Learning; LoRa communication; Context Aware Alert System; Real time monitoring