Artificial Intelligence for Sustainable Aquaculture and Fish Processing: Advancing Aquatic Health, Quality Assessment, and Value-Chain Optimization

Artificial intelligence (AI) is increasingly transforming fisheries and aquaculture by enabling data-driven approaches to improve aquatic animal health, production efficiency, resource management, and food quality. This review synthesizes recent advances in machine learning, deep learning, computer vision, and Internet of Things (IoT)-enabled technologies across aquaculture and post-harvest fish processing, with particular emphasis on their contributions to sustainable aquatic food systems. The review examines applications in real-time water-quality monitoring, early detection and prediction of fish diseases, behavioral and welfare assessment, biomass estimation, feeding optimization, growth prediction, and farm-level decision support. AI-enabled computer vision and sensor-based systems can facilitate non-invasive monitoring of fish health and production performance while improving feed-use efficiency and reducing environmental impacts associated with aquaculture operations. Beyond production, AI supports fish freshness assessment, species authentication, defect detection, shelf-life prediction, processing optimization, cold-chain management, and digital traceability, thereby reducing post-harvest losses and strengthening food safety. The review further highlights emerging integration of AI with edge computing, digital twins, multimodal sensing, and explainable AI to enable more autonomous and resilient aquaculture systems. Despite substantial progress, widespread adoption remains constrained by limited high-quality and representative datasets, lack of model transferability across species and production environments, infrastructure and connectivity limitations, low algorithmic interpretability, cybersecurity issues, data governance, and regulatory challenges. Future research should prioritize standardized datasets, explainable and energy-efficient AI, interoperable sensing platforms, and validation under diverse real-world aquaculture conditions. Overall, responsible integration of AI across aquaculture production and fish-processing chains offers significant potential to enhance aquatic health, resource-use efficiency, food safety, economic resilience, and the sustainability of global aquatic food systems.