Objective of the study was to develop a program for predicting the competitive performance of young athletes based on artificial intelligence technology. Methods and structure of the study. As part of the scientific work, the collection and processing of individual data of athletes (n=56) was carried out according to 38 characteristics, ranked into 2-4 categories in three key areas: heredity, environment and individual. Results and conclusions. As a result of data processing using deep neural networks and machine learning algorithms, two categories of prediction were identified: athletes who achieved a sports title or the highest category, and athletes who did not reach this level. The control testing of the created program showed only 11% of the error probability in predicting the competitive performance of young athletes. The author's program made it possible to identify reliable patterns: if a young athlete's mother has a sports title, then with 79% probability he will be effective in future competitive activities, and if he is still trained by a mentor with experience from 16 to 30 years, then the probability of reaching the highest level or sports title rises to 86%.
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