<p>Early identification of fish diseases is critical to maintain aquaculture and minimize its financial burden. In the diagnosis of diseases, time is critical and scientists’ experience is imperative, which makes the artificial intelligence-based models necessary. It is intended by this study to integrate deep learning with explainable artificial intelligence methods to establish an artificial intelligence-powered framework for early detection of disease in fish. In this study, we developed a hybrid artificial intelligence system designed to detect diseases in freshwater fish by analyzing skin images. The primary objectives of the system are to minimize economic losses, prevent further disease spread, and ultimately increase aquaculture productivity. For this purpose, we used a dataset consisting of images from seven different disease categories, with 250 images in each class. A new hybrid model, Residual Network-50–Vision Transformer, was developed by integrating the strengths of Residual Network-50 and Vision Transformer architectures to improve classification accuracy. The proposed model was subjected to rigorous comparison with established architectures, including Visual Geometry Group 16, Mobile Convolutional Neural Network Version 2, Efficient Neural Network–Baseline Model B0, standalone Vision Transformer, and the original Residual Network-50. The results demonstrated that Residual Network-50–Vision Transformer outperformed the model, achieving an impressive 99.14% accuracy rate. Furthermore, the study employed local interpretable model-independent explanations to explain which image features most significantly influence the model’s classification decisions, thereby enhancing the interpretability and confidence in the model’s output. The application of such advanced image processing models in combination with explainable AI techniques demonstrates significant potential to support sustainable aquaculture by reducing disease outbreaks and associated financial losses.</p>