The ocean is essential for sustaining global biodiversity, regulating climate, and supporting economic livelihoods. However, escalating pressures such as overfishing, pollution, and climate change threaten marine ecosystems worldwide. Addressing these complex and interconnected challenges requires advanced, adaptive tools for monitoring and decision-making. Artificial Intelligence (AI), including machine learning (ML) and deep learning (DL), has emerged as a transformative force in marine science, capable of revolutionizing biodiversity assessment, fisheries management, pollution detection, and climate impact forecasting. This review synthesizes recent advances in AI across major marine science applications, highlighting how data-driven models are being used to extract actionable knowledge from increasingly diverse and high-dimensional marine observations. Rather than focusing on individual algorithms, the review emphasizes common patterns in how AI enables large-scale biodiversity monitoring, adaptive resource management, and environmental risk assessment under data-limited and heterogeneous conditions. By critically examining both the capabilities and limitations of current approaches, this work identifies key structural challenges and emerging opportunities that will shape the future integration of AI into sustainable marine governance and policy-relevant decision-making.
Ho et al. (Wed,) studied this question.