The Wind Energy Prediction system is a machine learning-based application designed to forecast energy generation from wind turbines. It utilizes time-series data with features like time of day, wind speed, and temperature to predict future power output. The core machine learning model is based on XGBoost, which is trained on historical data and used for regression tasks to predict active power generation. The application is built using Flask, providing a user-friendly web interface for users to input date ranges and receive predictions. The results are visualized using Matplotlib and Seaborn, with a dynamically generated plot displayed on the web page. The pre-trained model is loaded using Pickle and performs predictions based on user input, with data points at a 10-minute interval. The system supports scalable and efficient predictions, leveraging Python libraries like Pandas for data manipulation and XGBoost for machine learning. Future improvements could include adding more environmental features, optimizing the model with advanced techniques, and deploying the system on a cloud platform for real-time prediction services.