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ABSTRACT The FAO's “blue transformation” roadmap necessitates a fundamental shift towards precision aquaculture to meet global food security targets while minimizing environmental footprints. This review provides a comprehensive overview of how artificial intelligence (AI) and decision support systems (DSS) serve as pivotal enablers for the “better production” objective outlined in the 2030 agenda. We critically examine the transition from empirical management to data‐driven operations, specifically evaluating the efficacy of predictive modeling in water quality control, biomass estimation, and disease forecasting. Beyond the technological capabilities of deep learning and hybrid architectures, this paper addresses the operational gap between high‐tech industrial solutions and the realities of small‐scale farming. Key challenges, including data heterogeneity, sensor reliability, and the socio‐economic “digital divide,” are identified as major barriers to widespread adoption. Consequently, we propose a strategic framework that integrates digital twins and accessible edge‐computing technologies, advocating for inclusive digital solutions that ensure resilience and sustainability across diverse aquaculture systems.
Öz et al. (2026) studied this question.