This project describes an integrated agricultural optimization system and supports farmers in making data-informed decisions that are productive and sustainable. The key methodologies used here are deep learning and machine learning tools to provide targeted support toward key agricultural areas, including soil classification, plant disease detection, fertilizer advisory, crop yield prediction, and optimal crop choice. This research first uses CNN to analyze soil images to classify the different types of soil and then gives us the optimal crop recommendations. The system’s disease detection module is also based on CNN which identifies plant diseases from leaf images, such as blight, rust, and leaf spot, enabling early detection that reduces yield loss and promotes produce quality. For every diagnosed disease, certain strategies are used to prevent further spread. Along with this, fertilizer recommendations are given to improve the health of crops. According to the diagnosis of the disease, a Linear Regression model further predicts crop yield with the help of certain parameters like location, crop, and seasonal parameters. Through this AI-based approach, farmers can select the right crops and control diseases by using resources effectively.