At the current stage of sports development, technical training within the long-term improvement system for race walking athletes should involve clearly defined model characteristics of their technical actions. The integration of advanced modeling technologies, such as artificial neural networks, allows for the creation of effective and high-quality models that represent athletes' technical actions. Objective: To improve the technical preparation of track-and-field athletes specializing in race walking by identifying model characteristics of technical actions within a long-term preparation framework. Materials and Methods: Neural network modeling of the technical actions of athletes specializing in race walking was performed based on data from biomechanical analyses of competitive exercise techniques. This data was collected via video recordings during the 2014-2021 Ukrainian Race Walking Championships, as well as at the Association of Balkan Athletic Federations Championships and the international "Evening Ivano-Frankivsk" Cup, organized in conjunction with Ukraine`s national championships. The studies included male athletes from various age groups competing at distances of 3 km, 10 km, and 20 km. In total, 98 analyses were conducted: 31 at 3 km, 36 at 10 km, and 31 at 20 km. Results: A total of 26 biomechanical indices of athletes' techniques were analyzed. Correlation analysis identified 14 key biomechanical characteristics that significantly influence performance outcomes within the long-term development system. Using these characteristics, along with indices of body length and mass, neural network models were developed to simulate and predict athletic performance. Conclusions: Artificial neural networks enabled the creation of models for race-walking technical actions that support high-level performance in young athletes aged 13-15 years (3 km distance), boys aged 16-19 years (10 km distance), and elite athletes aged 20 years and older (20 km distance).
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