Accurate prediction of crack width is essential for serviceability design and durability assessments of prestressed concrete structures. This study presents an explainable machine learning framework for predicting the maximum crack width of prestressed concrete beams based on experimental data. A comprehensive database of 404 specimens, including the bending moment, prestress index, load ratio, effective depth, and section stiffness, was constructed and enhanced through mechanically informed feature engineering. Random forest and XGBoost regression models were developed and systematically tuned using cross-validated hyperparameter optimization. Among the evaluated models, XGBoost achieved the highest predictive accuracy, with a coefficient of determination of 0.6507 and a root mean square error of 0.184 mm. Model interpretability was investigated using feature importance measures and SHapley Additive exPlanations, which identified the bending moment, load ratio, concrete compressive strength, and prestress index as the dominant factors influencing crack width. The observed relationships are consistent with the established flexural cracking theory, confirming that the proposed model captures physically meaningful behavior. The results demonstrate that combining explainable artificial intelligence with structural mechanics provides a robust and transparent tool for crack width prediction, offering valuable support for the performance-based evaluation and design of prestressed concrete members.