• Integration of AI, IoT, and Big Data is revolutionising automation and predictive control in aquaculture. • Advanced technologies, including federated learning, blockchain, and digital twins, enhance process optimisation. • Socioeconomic, infrastructural, and regulatory barriers impact large-scale AI implementation in aquaculture. The aquaculture sector is undergoing a rapid digital transformation, driven by the accelerated advancement of artificial intelligence (AI) technologies. This review examines how AI, alongside the Internet of Things (IoT) and Big Data, is reshaping smart aquafarming systems through intelligent automation, real-time decision-making, and predictive analytics. AI methodologies, such as machine learning, deep learning, attention-based models, and hybrid algorithms, are enabling significant improvements in water quality prediction, disease detection, feed optimisation, and behavioural analysis. The review also explores cutting-edge innovations beyond conventional AI applications. These include federated learning, explainable AI, edge AI deployment, and transfer learning, which address critical challenges related to data and system scalability in dynamic aquaculture environments. Particular emphasis is placed on the integration of AI with IoT systems and the development of digital twins to simulate and optimise aquaculture processes for predictive control. Furthermore, the potential of quantum AI and blockchain technologies are also assessed in the context of future-proof aquaculture systems. The review also provides a critical assessment of the socioeconomic barriers, infrastructural limitations, ethical considerations, and regulatory gaps that hinder the widespread implementation of AI—especially in rural and resource-constrained settings. This analysis presents a prospective framework for the development of scalable, resilient, and sustainable aquaculture systems empowered by next-generation AI technologies.
Ratan et al. (2026) studied this question.