Deep Transfer Learning (DTL) proved to be a powerful method in image-based disease detection, especially in fields limited by minimal annotated images like agriculture and medical diagnosis. In this paper, a DTL-driven method for plant leaf disease classification is proposed using pre-trained convolution neural networks (CNNs), including DenseNet121, EfficientNetB0, and ResNet50. These models were trained fine with a two-stage transfer learning method—preliminary feature extraction followed by fine-tuning towards specific fine-tuning—to fine-tune them to a domain-specific dataset sampled from the PlantVillage repository. A subset of 3,171 images of apple leaves with four different classes (Apple Scab, Apple Black Rot, Cedar Apple Rust, and Healthy) was employed for testing. Experimental findings indicated DenseNet121 achieved better generalization performance, with a validation accuracy of 92.5% and the best validation loss of 2.31. The research illustrates that pre-trained models, when fine-tuned properly, can be very effective in identifying plant diseases and play a very important role in the development of automated, scalable, and accurate decision-support systems for precision agriculture. Future research will emphasize combining ensemble methods, explainable AI, and edge device deployment for real-time processing.