Basic military training (BMT) is important and initial period for soldiers, especially the military basic throwing performance is one of the key military tactical motor skill. In recent years, machine learning (ML) techniques have been increasingly adopted to analyze extensive datasets and derive meaningful insights. To evaluate the effectiveness of fitness training strategies for military throwing, this study analyzed seven common input features: pull-ups, push-ups, squat concentric/eccentric peak power, squat maximum strength, leg tuck, and a 3 km run. Four data mining models—Random Forest, Multilayer Perceptron, AdaboostM1, and Bagging classifier—were tested to identify the most effective method for predicting performance. The Random Forest model excelled, achieving the highest accuracy, precision, recall, and F1 score, indicating its superiority in this context. These seven attributes were thus identified as key predictors of military throwing performance. However, further research is necessary to establish a definitive ranking and to fully understand the importance of each feature in refining training strategies.