Artificial Intelligence in Sustainable Aquaculture: Transforming Fish Disease Diagnosis, Predictive Analytics, and Treatment Optimization

Abstract

Prevalence of fish diseases have been on the rise posing a marked threat to the aquaculture industry world-wide. Following the challenges associated with traditional detection and treatment of diseases, Artificial Intelligence (AI) has emerged as a powerful tool for improving disease diagnosis, monitoring, and treatment. This review systematically reviewed most recent AI advancements in the detection, predictive modelling, and treatment optimization of diseases in fish. The study used IEEE Xplore, Web of Science, pubmed, and Scopus, for a thorough search with focus on researches published between 2020 and 2025. Exclusion criteria excluded studies without quantitative analysis, while inclusion criteria mandated studies using AI-driven disease detection, treatment, and management techniques. Results show that AI greatly improves fish survival rates, treatment effectiveness, and disease diagnosis accuracy. Machine learning models, such as convolutional neural networks (CNNs), reinforcement learning techniques, and others, have been extremely beneficial for treatment optimization, fish monitoring, and disease outbreak prediction. The integration of AI in fish health management and disease prevention, according to the review's findings has high potential to change traditional aquaculture making it more sustainable.