Objectives: Timely identification of endometrial nonbenign lesions led to improved outcomes, but there was a lack of effective predictive models for asymptomatic endometrial thickening. The aim of this study was to develop a strong machine learning (ML) model for assessing the risk of endometrial malignancy in asymptomatic patients after menopause. Methods: This retrospective study was designed to collect data from 971 postmenopausal asymptomatic women with endometrial thickening. The bootstrap resampling method was used for model training, internal validation, and external validation. With 41 easily accessible characteristics, multifactor regression and least absolute shrinkage and selection operator regression were performed for feature selection. Nine ML algorithms were applied to build a model. To explain the final model and rank feature importance, the SHapley Additive exPlanation (SHAP) method was utilized. Meanwhile, a nomogram was developed to facilitate model interpretation. Results: The comprehensive methodologies identified parity, Doppler flow signals, endometrial thickness, cancer antigen 125, and D-dimer as significant predictors. The logistic regression (LR) model demonstrated superior performance compared with other ML algorithms, achieving an accuracy of 88%, a sensitivity of 78%, a specificity of 98%, and an area under the receiver operating characteristic curve of 0.81. Furthermore, individualized predictions of endometrial malignancy were visualized through a force plot generated by SHAP analysis. A nomogram based on the LR model was subsequently constructed, showing area under the receiver operating characteristic curve values of 0.82, 0.82, and 0.81 for the training, internal validation, and external validation cohorts, respectively. The calibration curve demonstrated excellent consistency. Conclusions: We developed an LR-based nomogram model and interpreted using the SHAP method, which provided visual insights for detecting endometrial nonbenign lesions in asymptomatic postmenopausal women. This approach would aid clinicians in providing individualized treatment and help avoid unnecessary invasive surgeries.