Rice serves as a fundamental food crop for more than half of the global population, particularly in agrarian countries like Bangladesh. However, fungal diseases such as leaf blast, neck blast, and node blast, caused by Magnaporthe oryzae, threaten rice production and endanger food security. This study proposes an automated detection system of rice leaf disease using deep transfer learning and multi-level feature extraction to improve early diagnosis accuracy. A real-time dataset of 1,500 annotated rice leaf images was col-lected from the fields of Sherpur and Mymensingh and categorized into three major disease classes. Preprocessing techniques, including resizing, normalization, grayscale conversion, Gaussian blur, and advanced augmentation methods, were applied to enhance dataset quality and diversity. Six states of the art pretrained Convolutional Neural Network (CNN) models Efficient-NetV2S, ResNet50V2, MobileNetV2, VGG16, DenseNet121, and Xception were fine-tuned and evaluated. Feature extraction was performed at multiple levels to capture detailed disease characteristics. Among the models, Effi-cientNetV2S achieved the highest classification accuracy of 99.28%, outperforming others in both generalization and training stability. This work establishes the foundation for scalable, cost-effective diagnostic systems that can directly empower farmers in developing regions with timely disease detection capabilities.