Objective A Cobb angle prediction model for adolescent idiopathic scoliosis (AIS) based on three-point mechanical data from three-dimensional (3D)-printed orthotics and various machine learning algorithms was developed, so as to provide an innovative, radiation-free method for early clinical screening and monitoring of AIS. Methods Clinical 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. Six algorithms, namely, random forest, support vector regression, gradient boosting regressor, extreme gradient boosting, lightgbm, and catboost, were used to construct and evaluate the performance of Cobb angle prediction models. Results The gradient boosting regressor model had the best performance on several evaluation metrics, achieving a precision rate of 0.937, recall rate of 0.818, F1-score of 0.949, and an area under curve (AUC) value of 0.843. In the validation set, the model’s predictions reached an accuracy rate 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 a great clinical significance.