In this study, a deep learning-based web application aimed at a classification of canine skin conditions using dermal images is presented. The paper elaborates on how the ResNet-50 architecture is exploited to educate the model in recognizing six basic skin conditions in dogs. The Dog-centric Skin Disease Classification System includes Dermatitis, Fungal Infections, Healthy Skin, Hypersensitivity, Demodicosis, and Ringworm. AI-based technologies like CNN are incorporated into the web app to avoid human error, increase the accuracy of diagnosis, make the diagnosis process faster, and make the treatment process more accurate. The problems such as the web server responding with the expected code, the client-side components executing as they should, and all the visual elements rendering correctly are resolved. Self-trained neural network (CNN), a reinforcement-type chatbot, and diagnostic data storage solutions from Gemini run side by side. The system is designed so that it processes the image millions of times to get the best possible answer from all the probabilities of the disease mentioned. How about having a conversation structure plug in the most probable issue and the corresponding therapy from the clinical non-technical staff most possibly over the phone? As shown in a preliminary study, high classification performance without any confusion among different inputs is a good sign of model stability. There is a compelling demonstration of how CNN-based architectures like ResNet-50 can be beneficial in veterinary diagnostics by David Marquardt, Priscilla Rizal, and Anifah Lestari. Their findings indicate that these models would serve as the foundation of studies involving more extensive datasets, cross-breed universality, and clinical embedding in the future.