The prevalence of diseases among fish represents a profound and pervasive risk to the aquaculture sector, demanding swift and accurate identification for containment. Epizootic Ulcerative Syndrome (EUS), a deceptive “red spot disease,” is a prominent concern. This paper presents a comprehensive solution employing advanced technologies to promote sustainable aquaculture practices. Sustainable aquaculture utilizes a two-phase approach, combining image enhancement with Convolutional Neural Network (CNN) and Support Vector Machine (SVM) methodologies for disease classification. The CNN model achieves an impressive 99.34% accuracy, augmented for robustness, while SVM demonstrates efficacy in early disease detection. By leveraging computer vision and deep learning algorithms, sustainable aquaculture facilitates real-time disease detection through a user-friendly web application, bridging theory and practice in fisheries management. This holistic approach not only enhances the health and productivity of fish farms but also underscores a pivotal advancement in automating disease identification. Sustainable aquaculture heralds a promising future where aquatic health and economic viability harmoniously thrive, safeguarding both livelihoods and nutritional resources in aquaculture ecosystems.