Clutch moments often determine the final outcome of basketball games. The present study proposes an innovative analytical framework that integrates machine learning with explainable techniques to identify and quantify the influence of technical–tactical indicators on possession-level scoring outcomes during clutch moments. Using an observational methodology, 607 clutch offensive possessions from 41 games of the 2019 and 2023 FIBA Men’s Basketball World Cups were coded and analyzed. First, chi-square tests were applied to examine associations between technical–tactical variables and offensive outcomes (scoring vs. non-scoring). Subsequently, ten categories of machine learning models were systematically compared, with the Random Forest identified as the best-performing model for predicting scoring outcomes in clutch possessions. Finally, SHAP values were used to interpret the contribution and directional effects of each variable on model outputs. The results indicated that play-types (p < 0.001), possession duration (p = 0.009), and ending zone (p = 0.001) were significantly associated with scoring success. SHAP analysis further showed that “restricted area” “possessions completed within 1–8 seconds,” and “ending player position_center” were positively associated with scoring success. These findings provide data-driven support and practical reference for coaches and performance analysts when designing training priorities and tactical strategies in clutch contexts. Specifically, during clutch moments of elite international competition, offenses may benefit from selectively increasing tempo to break deadlocks and emphasizing team-oriented actions by increasing center involvement as primary finishers and strengthening screening and pick-and-roll actions.