not-yet-known not-yet-known not-yet-known unknown Background： Pediatric osteosarcoma treatment with high-dose methotrexate (HD-MTX) risks delayed clearance due to immature organ function. Interpretable machine learning enables proactive prediction, improving monitoring and reducing toxicity risks effectively. Methods: This retrospective study (2020–2024) on pediatric patients with osteosarcoma treated with HD-MTX aimed to develop robust predictive models. Feature selection was conducted using LASSO regression, followed by 10-fold cross-validation and hyperparameter optimization to enhance model stability and generalizability. Predictive models, including LASSO, ridge, and logistic regression, were developed and rigorously compared to identify the best-performing model. Shapley additive explanations (SHAP) values were used to interpret predictor contributions and relative importance. Additionally, a Shiny-based interactive visualization tool was created for user-friendly clinical integration and data-driven decision-making. Results: This study analyzed 181 pediatric osteosarcoma cases treated with HD-MTX, with 51 experiencing delayed MTX elimination. The dataset was divided into training (117 cases) and test (64 cases) sets, maintaining proportional class distributions. LASSO regression identified seven key predictors through cross-validated error minimization. Three machine learning models (LASSO, logistic regression, ridge regression) were developed. LASSO outperformed the others, achieving an area under the curve (AUC) of 0.8442 across multiple metrics, including ROC, F1 score, and decision curve analysis. Calibration analysis confirmed superior predictive sensitivity for delayed MTX elimination. SHAP analysis ranked Methotrexate Concentration at 3 Hours (MTX3H) as the most critical feature. An interactive Shiny web application was developed to provide personalized predictions and insights into predictor contributions, supporting clinical integration and decision-making. The application is accessible at: https://sclslc.shinyapps.io/shiny_cls2_1model_dalex/. Conclusion： This study presents an interpretable machine-learning model for predicting delayed MTX elimination during high-dose MTX chemotherapy in children with osteosarcoma. Deployed as a web-based tool, the model enables personalized predictions, enhances patient monitoring, reduces toxicity risks, and supports evidence-based clinical decision-making.