Abstract: Fish plays an important role in the marine ecosystem and is one of the main sources of protein for humans. One of the key issues in fishery resource exploration is the accurate identification of fish species and the correct discrimination of fish stocks. In the context of big data, machine learning techniques as emerging data processing techniques have gradually replaced traditional methods. Compared with traditional data analysis, machine learning has shown the advantages of high accuracy, high robustness and high efficiency while dealing with massive and high-dimensional ocean data. Its advantages are gradually recognized in the field of marine biology and ecology. This review firstly introduces that the current focus of fish study which has migrated to machine learning, then summarizes the applications of machine learning in fish species identification and stock discrimination in terms of data sources, feature selection, and classifiers. This review then introduces application scenarios of various deep learning neural networks, with Convolutional Neural Networks as representative, in fish species identification. The advantages and disadvantages of each classifier and the traits of fish species that suits to those classifiers are summarized from the perspective of predictability, expandability, and data sensitivity. Finally, common metrics for currently evaluating the effectiveness of models are summarized. The characteristics of ecological resource data and the development status of deep learning in the era of big data are synthesized, and the problems and challenges of the applications of machine learning in fish species identification and fish stock discrimination are also summarized.