Rice is one of the most widely cultivated crops in India, playing a crucial role in the country's agricultural economy. However, rice production is frequently threatened by diseases, such as Bacterial Leaf Blight, Brown Spots, and Sheath Blight, which can significantly reduce yield. This project focuses on developing a deep learning based rice leaf disease detection system using Convolutional Neural Networks (CNN). A dataset containing images of rice leaves, categorized into different disease types and healthy leaves, was used to train the model. By applying advanced image processing and deep learning techniques, the system can accurately identify and classify diseases. A user-friendly web application allows farmers to upload images and receive real-time diagnostic feedback, empowering them to implement timely corrective measures. This system offers a scalable and cost-effective solution for enhancing crop management, supporting food security, and promoting sustainable agriculture in India.