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Abstract Background Osteoporosis is a major health concern in Vietnam due to a rise in aging rates. However, cost-effective early screening tools tailored to the Vietnamese population are lacking. In this study, we applied a machine learning approach to develop self-assessment tools for osteoporosis in menopausal women. Methods We used retrospective data from 15,670 postmenopausal Vietnamese women extracted from electronic medical records. Bone mineral density (BMD) measurements of the lumbar vertebrae (L1–L4) and the left and right femoral necks were obtained using the dual-energy X-ray absorptiometry (DXA) system. Osteoporosis was defined as a BMD T-score of < -2.5. Eight algorithms (Logistic Regression, Decision Trees, Random Forest, XGBoost, K-Nearest Neighbors, Neural Networks, Lasso and Ridge regressions, and Naïve Bayes) were utilized to develop prediction algorithms for each anatomical site. The main predictors included age, menopause age, weight, height, lifestyles, medical history and comorbidities. Cross-validation was employed to prevent overfitting. Results The prevalence of osteoporosis, as determined by BMD, varied across anatomical sites, ranging from 22% at the left and right femoral necks to 18.3–35.5% across the lumbar vertebrae. The performance of the models varied slightly among algorithms, although the differences were not substantial. Considering the balance between performance and simplicity, logistic regression was selected as the final algorithm. The final models were developed using four predictors: age, menopause age, height, and BMI, with the area under the curve (AUC) ranging from 0.75 at lumbar vertebrae L4 to 0.84 at right femur neck. Our models demonstrated superior performance compared to existing tools, such as OSTA, which were developed for the general Asian population. Conclusion The newly developed self-assessment tools were shown to be simple and effective in predicting osteoporosis among postmenopausal Vietnamese women. These tools have the potential to serve as early screening instruments for osteoporosis, particularly in resource-limited settings.