Beyond silos: An integrated AI-blockchain framework for sustainable aquaculture in Ghana

• Introduces a novel AI-blockchain architecture for verified precision aquaculture. • Develops an edge-computing-aware framework for resource-constrained environments. • Proposes predictive analytics for aquaculture optimisation and yield forecasting. • Details a phased implementation roadmap with mobile-first solutions. • Demonstrates the framework's applicability through a Ghana case study. The application of Artificial Intelligence (AI) and blockchain in aquaculture remains technologically siloed, creating a significant gap between predictive capabilities and verifiable trust. This study makes a novel contribution by proposing and critically examining a fully integrated socio-technical framework that moves beyond the technological dichotomy prevalent in the literature. Through a systematic review of 36 peer-reviewed studies, we move beyond technological solutionism to construct a socio-technical model that demonstrates the synergistic interdependence of these technologies: AI's predictive power for. Contextualised for Ghana's aquaculture sector—a setting characterised by high import dependency and smallholder dominance—our findings yield a distinctive, phased implementation roadmap. This roadmap prioritises mobile-first, offline-capable solutions and cooperative governance to ensure inclusivity. Our study provides two primary scientific contributions: (1) the novel conceptualisation of "verified precision aquaculture" as a paradigm enabled by deep AI-blockchain integration, and (2) a contextually grounded, phased implementation roadmap that translates cyber-physical systems theory into an actionable strategy for resource-constrained environments.