Rice (Oryza sativa) is the most important grain crop globally. It serves as a primary food source and energy provider for greater than 50% of all people on Earth. Agriculture is a major part of many nations’ economies, and it plays a crucial role in Telangana. Rice, also referred to as paddy, is cultivated as a staple food and cash crop, as rice is consumed three times a day by the people of Telangana. However, various factors, such as diseases affecting paddy leaves, temperature fluctuations, and pest attacks, can significantly impact paddy production, potentially reducing yields by 40% to 50%. The prevalence of paddy diseases varies by region and season, with the most common being paddy leaf blast area, Paddy Leaf Scald, paddy leaf smut, and rice blast. Early detection of these diseases is essential to prevent widespread damage to farmlands. We address this through our proposed system, which uses preprocessing and data augmentation to extract the dataset. It employs pre-trained Inception V3 and deep learning-based CNN algorithms, efficiently classifying paddy diseases. This approach enhances the accuracy of disease prediction and reduces the time required for classification, helping farmers take timely action to protect their crops.