Rainfall Prediction using Machine Learning. This project aims to develop a predictive model for rainfall forecasting using historical weather data. The primary objective was to analyze key meteorological parameters such as temperature, humidity, wind speed, and pressure to accurately predict rainfall. By applying machine learning algorithms like Linear Regression and Random Forest, the system was trained and tested to achieve reliable accuracy. Data preprocessing techniques such as handling missing values, normalization, and feature engineering were implemented to improve performance. The model’s predictions were visualized through graphs, enabling better interpretation of weather patterns. This solution demonstrates how AI and data-driven approaches can be leveraged for agricultural planning, water management, and disaster preparedness, contributing to sustainable development and smart environmental management.