_{：Objective To develop a Cobb angle prediction model for adolescent idiopathic scoliosis (AIS) based on three-point mechanical data from 3D-printed orthotics and various machine learning algorithms, providing an innovative, radiation-free method for early clinical screening and monitoring of AIS.MethodsClinical data from AIS patients and mechanical data from 3D-printed orthotics were collected to construct a comprehensive dataset with features such as gender, age, disease type, weight, and Risser score. Using six algorithms—Random Forest, Support Vector Regression, Gradient Boosting, and others—to construct and evaluate the performance of Cobb angle prediction models.ResultsThe Gradient Boosting model outperformed others in terms of accuracy, precision, and F1-Score, while the CatBoost model also showed excellent performance in accuracy and AUC. The Gradient Boosting model achieved an accuracy of 0.942, fitting well with the actual Cobb values.Conclusion The Cobb angle prediction model based on mechanical data and machine learning effectively avoids the radiation risks associated with traditional full-spine X-ray examinations in early clinical screening. It provides a non-invasive assessment for AIS patients, enhancing the safety and efficiency of screening and monitoring, and offering a powerful decision-making tool for clinicians, with significant clinical implications.}