Cloud Enabled IoT Weather Station with Predictive Analytics Using Machine Learning

This paper presents an Integrated Internet of Things (IoT) Based Weather Monitoring System combined with a Machine Learning (ML) Based Weather Prediction Model for local atmospheric analysis and forecasting. The system utilizes a network of environmental sensors, including BME680, wind speed and direction sensors, and rainfall sensors, to collect real-time temperature, humidity, pressure, and air quality data. These data are transmitted using NodeMCUESP32 over WiFi and stored on a cloud database for remote access and analysis. A machine learning model trained on historical and real-time sensor data predicts short-term weather patterns, offering improved accuracy and rapid feedback. This integrated system aims to provide a cost effective, scalable, and reliable weather monitoring platform for rural and urban applications. The results demonstrate that IoT based monitoring combined with ML prediction enhances the efficiency, automation, and reliability of weather forecasting.