Advances in data management technologies are being adapted to resolve difficulties and impacts that aquaculture manifests, some aspects that over the years have not been fully managed, are now more feasible to solve, such as the optimization of variables that intervene in the growth and increase of biomass, the prediction of water quality parameters to manage and make decisions during farming fish, the evaluation of the aquaculture environment and the impact generated by aquaculture, the diagnosis of diseases in aquaculture fish to determine more specific treatments, handling, management and closure of aquaculture farms. The objective of this article was to review within the last 20 years the various techniques, methodologies, models, algorithms, software, and devices that are used within artificial intelligence, machine learning and deep learning systems, to solve in a simpler way, quickly and precisely the difficulties and impacts that aquaculture manifests. In addition, the fundamentals of artificial intelligence, automatic learning and deep learning are explained, as well as the recommendations for future study on areas of interest in aquaculture, such as the reduction of production costs through the optimization of feeding based on good aquaculture practices and parameters of water quality, the identification of sex in fish that do not present sexual dimorphism, the determination of quality attributes such as the degree of pigmentation in salmon and trout.