A review argues that advances in artificial intelligence, machine learning and materials informatics could accelerate the discovery and optimisation of sustainable marine antifouling coatings by predicting performance, identifying structure-property relationships and reducing experimental effort, since their development still relies largely on trial-and-error approaches despite progress in sustainable antifouling coatings. Marine biofouling has significant economic and environmental consequences, including increased drag, higher fuel consumption, elevated emissions, additional maintenance costs and accelerated material degradation. However, the direct application of AI and ML specifically to marine antifouling coating design remains relatively limited, and much of the available evidence originates from broader materials science and coatings research, requiring careful evaluation when transferring approaches to this complex application area. The review critically distinguishes between directly demonstrated antifouling applications and transferable approaches from related materials systems, focusing on how AI and ML can be applied to predict fouling resistance, durability, corrosion protection and environmental sustainability. Key challenges identified include data scarcity, heterogeneous experimental datasets, the absence of standardised testing protocols, limited field validation and the need for improved model interpretability. Future opportunities highlighted include digital twins, explainable AI, generative AI and autonomous materials discovery platforms, and the author proposes a staged pathway from standardised data generation and predictive modelling toward experimental validation, active learning and ultimately autonomous materials discovery, requiring coordinated efforts across data generation, protocol standardisation and model development.