Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite club, external load (total distance, high-speed running, mechanical work) and heart rate were collected in training. Machine-learning-derived fitness and fatigue indices were computed and combined with 7- and 28-day load variables in a Random Forest regression model predicting match minutes. The trained model was then used to simulate four fatigue conditions by fixing the match-day fatigue index (z-FA_match = 0, −1, −2, −3). In an independent test season, the model showed a mean absolute error of 22.5 min and R² = 0.17 for playing time prediction, with z-FA_match as the most influential predictor. Simulated fatigue thresholds occurred in an ordered way (0 = 57.1, −1 = 64.9, −2 = 84.8, −3 = 84.4) and differed across season period, playing position, overall seasonal minutes, and return-to-play status. Integrating external load with fitness and fatigue indices via machine learning can provide individualised estimates of when players are likely to reach fatigue states, supporting decisions on selection, substitutions, and return-to-play management.