The concerns of environmental degradation and climate change have recently been the focus of intense attempts to apply creative solutions from AI, IoT, data science, and big data. Effective rainfall prediction and classification are crucial for various sectors. However, traditional forecasting methods often struggle to accurately capture the complex dynamics of rainfall patterns, particularly in regions with diverse weather conditions. In response to these challenges, we propose a novel “Rainfall Classification for Agricultural Sustainability” (RCAS) approach, which utilizes advanced machine learning (ML) techniques, specifically XGBoost, to classify rainfall patterns based on historical meteorological data. By leveraging feature engineering and model optimization strategies, RCAS identifies and categorizes rainfall events into distinct classes, facilitating more informed decision-making for stakeholders. Predicting wind turbine energy is essential for optimizing renewable energy utilization and ensuring grid stability. Additionally, precise predictions support efficient grid management, allowing utilities to balance supply and demand in real time, ultimately enhancing energy reliability and sustainability. This study bridges the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. We emphasize the importance of accuracy in these predictions, aiming to overcome current standards. Our approach leverages these models to predict wind power generation for the next 15 days, utilizing the SCADA Turkey dataset and Tatapower Poolavadi Dataset. R2 score is used alongside traditional metrics like mean absolute error (MAE) and root mean square error (RMSE) to evaluate model performance. Leveraging these methodologies aimed to enhance the accuracy of wind power forecasting, enabling more efficient utilization of renewable energy resources.