AI-Driven Digital Twin Framework for Sustainable Fisheries Management and Intelligent Aquatic Water Resource Monitoring

Digital twins are increasingly proposed as a means of linking aquatic observations, predictive models and management decisions. Their relevance to sustainability nevertheless depends on more than the speed or accuracy of an artificial intelligence model. This critical narrative review examines the evidence connecting digital-twin architectures with capture fisheries, aquaculture and freshwater and coastal water-quality monitoring. Literature published through 30 July 2026 was selected through targeted scholarly searches, citation tracing and bibliographic verification, with particular attention to observation quality, independent validation, operational feedback and management outcomes. The synthesis distinguishes a digital model, a continuously updated digital representation and a decision-linked twin, and separates component performance from demonstrated ecological benefit. Evidence supports advances in image-based monitoring, satellite retrievals, hybrid environmental prediction and selected field deployments. It is substantially less conclusive regarding sustained stock recovery, equitable allocation or ecosystem-scale benefits attributable to a complete digital-twin system. Important limitations include imperfect observability, inappropriate spatial and temporal validation, uncertain transfer between environments, simulator-dependent policy optimisation and incomplete treatment of human behaviour. Apparent disagreement between promising technical demonstrations and limited management evidence largely reflects different endpoints and system boundaries rather than a simple conflict over technological feasibility. A federated, decision-centred framework is proposed in which traceable observations support uncertainty-aware state estimation, explicit management objectives, constrained decision support and independently audited outcomes. The framework includes staged deployment, human authorisation, local data governance and lifecycle assessment; it is an unvalidated synthesis rather than an implemented system. Near-term priorities are prospective forecasting benchmarks, comparative management trials, failure-tolerant monitoring and participatory evaluation in data-limited fisheries. Digital twins are most defensible as accountable extensions of existing environmental observation and adaptive management, not as substitutes for ecological understanding, enforceable institutions or legitimate collective decisions.