Performance in elite judo is shaped by the interplay between precise technical execution and stable psychological functioning under competitive pressure. While prior work has examined technical-tactical patterns or psychological variables in isolation, an integrated predictive framework combining both domains with artificial intelligence methods has not been systematically developed. This study analyzes how motion pattern indicators derived from video-based pose estimation and quantified psychological resilience indices jointly predict technical-tactical outcome in elite-level competition. A systematic literature review of 20 peer-reviewed sources from Scopus, Web of Science, SpringerLink, and PubMed databases published within the past five years was conducted, supplemented by observational case data from elite tournament settings. An original multi-modal prediction architecture is proposed that fuses skeletal motion features with resilience scores in a hybrid gradient boosting and long short-term memory (LSTM) model. The framework reached predictive accuracy of 90% in analogous biometric integration studies. Findings show that elite judokas demonstrate both faster motor anticipation and significantly higher resilience scores compared to non-elite athletes, and that the combined model outperforms single-modality baselines. Practical recommendations for coaching integration are provided. This work will interest sports scientists, performance analysts, judo coaches, and researchers working at the intersection of sports technology and applied psychology.

judo, technical-tactical performance, motion pattern analysis, psychological resilience, artificial intelligence, machine learning, pose estimation, LSTM, performance prediction, elite sports

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