In order to explore the application and development of artificial intelligence （AI） in the fisheries sector， this study systematically reviews the innovative application scenarios， challenges， and future directions of AI in key segments of the field. Existing research indicates that the core AI technologies applied in fisheries primarily include computer vision， machine learning， and deep learning. Their applications cover critical areas such as breeding， aquaculture， disease prevention and control， processing and quality safety， resource monitoring. Key research focuses include： utilizing AI technologies for fisheries breeding； achieving intelligent feeding in the aquaculture process， real-time analysis of water quality factor data， and assessment of the aquaculture environment； rapid diagnosis of common aquatic diseases， automatic identification of abnormalities in farmed organisms， and issuing warnings； precise automated sorting and grading of aquatic products， guiding robots in accurate slaughtering and segmentation； and real-time dynamic monitoring of fishery resources. In response to current limitations in AI applications such as scarcity of high-quality data， insufficient model generalization capability， and a shortage of interdisciplinary talent， the study suggests that future efforts should focus on the following tasks： （1） Integrating AI with technologies such as gene editing and genomic selection to promote the breeding of new varieties with disease resistance， high yield， and superior quality； （2） Combining AI with Internet of Things technologies to develop practical multi-factor aquaculture prediction methods and models， enabling intelligent and precise feeding； （3） Experimenting with establishing various BP neural network models or adopting large vision models for disease prediction； （4） Integrating AI， robotics， Internet of Things， and other technologies to build an intelligent aquatic product processing system， and utilizing blockchain technology to achieve full-process traceability； （5） Emphasizing the establishment of standard databases， incorporating distinct morphological characteristics of species into learning models to improve the accuracy of identification. With the continuous advancement of AI technology， its application scenarios in the fisheries sector will continue to expand. This not only aligns with China’s requirements for resource-saving and environmentally sustainable development in fisheries， but also helps promote the digitalization， precision， and intelligent development of China’s fisheries industry.