Transformative artificial intelligence integration in aquatic supply chains: synergizing precision aquaculture with intelligent logistics and data-driven consumption

Aquatic supply chains currently encounter critical sustainability and efficiency challenges, ranging from resource overexploitation to inherent cold chain vulnerabilities. Although individual artificial intelligence (AI) applications are emerging, the research synthesizing the entire “Farm-to-Table” continuum remains scarce. This review bridges this gap by evaluating AI integration—specifically machine learning and deep learning—across aquaculture, harvesting, processing, logistics, and marketing. The analysis reveals that while AI demonstrates notable efficacy in precision tasks like dynamic water quality prediction and automated catch classification, applications in pre-processing and low-altitude delivery remain nascent. Future advancements require interpretable algorithms, standardized databases, interdisciplinary collaboration, and cost-effective deployment to construct resilient, intelligent supply chains that ensure food safety and satisfy growing global market demands. Ultimately, this review provides a robust theoretical foundation for researchers and practitioners to enhance product safety and operational efficiency, fostering a sustainable, digital transformation of the aquatic industry. • AI optimizes end-to-end aquatic chains via precision aquaculture & smart logistics. • DL achieves high accuracy in water quality forecasting and disease diagnosis. • Vision-AI behavioral analytics reduces feeding waste in aquaculture. • SERS-1D CNN enables reliable detection of antibiotic residues for safety inspection. • Blockchain-AI synergies enhance cold-chain transparency and minimize quality loss.