Canine skin diseases, including canine scabies, fungal infections, and hypersensitivity allergies, are prevalent and require accurate diagnosis for effective treatment. However, traditional veterinary diagnostic methods face challenges such as similar clinical symptoms, limited accessibility to veterinary professionals, and misdiagnosis due to lack of expertise, particularly in rural areas of Sri Lanka. To address these challenges, the study explores deep learning-based classification for automated detection, addressing limitations in traditional veterinary diagnostics. A custom dataset of 2,511 images was collected and augmented to 12,565 images to improve model generalization. A comparative analysis was conducted using five deep learning models, including a baseline CNN, Hybrid CNN + ResNet50, ResNet152V2, EfficientNetB1, and MobileNetV3. The models were trained and evaluated based on their accuracy, loss, and generalization performance. The results indicate that EfficientNetB1 achieved the highest validation accuracy (98.96%) with a low loss (0.1278), followed closely by MobileNetV3 (98.76% accuracy). The Hybrid CNN + ResNet50 model balanced accuracy and efficiency, whereas the baseline CNN exhibited overfitting. Findings suggest EfficientNet-B1 and MobileNetV3 as optimal models for real-world deployment due to their accuracy and computational efficiency. Future work will focus on expanding the dataset, improving model interpretability using explainable AI techniques, and optimizing models for real-time deployment in veterinary applications. This research contributes to the advancement of AI-powered diagnostic tools that can assist veterinarians and pet owners in early and accurate detection of canine skin diseases.