This research presents a comprehensive study on the application of deep learning techniques for the detection and classification of rice leaf diseases. The objective of this study was to develop an accurate and reliable model for automated disease diagnosis, which can aid in early detection and effective management of rice crop diseases. The research employed a dataset consisting of 2,627 images of six different rice leaf diseases, namely Bacterial Leaf Blight, Brown Spot, Healthy, Leaf Blast, Leaf Scald, and Narrow Brown Spot. The dataset was collected from Kaggle.com and underwent rigorous preprocessing steps to enhance the quality and suitability for training the models. Two transfer learning models, namely VGG19 and MobileNetV2, were selected and trained using the preprocessed dataset. The models were fine-tuned by freezing the pre-trained layers and adding additional layers for classification. The performance of each model was evaluated using various metrics, including accuracy, precision, recall, and F1 score. The results demonstrated the effectiveness of the proposed approach in accurately diagnosing rice leaf diseases. The MobileNetV2 model achieved an overall accuracy of 92.4%, outperforming the VGG19 model, which achieved an accuracy of 90.5%.