As global climate change intensifies and uncertainty in the spatiotemporal distribution of fishery increases， artificial intelligence （AI） is becoming an important force inadvancing marine fisheries forecasting technologies， owing to its robust capability in representing high-dimensional spatiotemporal data. This paper systematically reviews the application and research progress of AI in marine fisheries forecasting. First， a standardized AI-based forecasting framework is outlined， encompassing multi-source data integration and preprocessing， model development and training， and model performance evaluation. Second， representative studies of AI in forecasting fisheries for major oceanic commercial species， particularly squid and tuna， are reviewed， highlighting the advantages of ensemble learning and deep learning in modeling complex nonlinear responses to environmental variability. Finally， three key scientific issues are discussed in depth， including spatiotemporal scale optimization， optimal model selection， and response mechanisms to climate change. With the continuous advancement of AI， future fisheries forecasting is expected to evolve toward multi-task joint forecasting and probabilistic forecasting， thereby providing theoretical support for the development of intelligent fisheries forecasting systems with high accuracy， strong robustness， and enhanced ecological interpretability， as well as autonomous decision-making agents.