Objective Based on clinical data from patients with osteoporosis, five machine learning algorithms are used to construct osteoporotic fracture risk prediction models, and their performance is evaluated and compared. Methods A total of 333 consecutive patients with osteoporosis (66 males and 267 females; median age, 69 years) who attended Song-jiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2020 to December 2025 were selected according to inclusion and exclusion criteria. Patients were divided into a fracture group (n=146) and a non-fracture group (n=187) based on the presence or absence of a history of fragility fractures. Baseline patient data and laboratory indicators were collected, and missing data were handled using multiple imputation by chained equations. Relevant studies were conducted using the R programming language. Feature variables were screened using logistic regression and lasso regression, and variable importance was ranked using random forest. Data were split into a training set and a test set at a 7∶3 ratio. Models were constructed using logistic regression, decision tree (DT), random forest, support vector machine, and extreme gradient boosting (XGBoost) algorithms. The performance of the models in predicting osteoporotic fracture risk was evaluated using indicators such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. The optimal model was interpreted using SHAP (SHapley Additive exPlanations) values. Results Univaria-te logistic regression analysis showed that sex, parathyroid hormone (PTH), N-terminal mid-fragment osteocalcin (N-MID-OT), alkaline phosphatase, albumin, calcium, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), hemoglobin, and phosphorus were significantly associated with osteoporotic fractures. Lasso regression identified seven key features: sex, PTH, N-MID-OT, albumin, calcium, MCHC, and phosphorus. After random forest importance ranking, six variables-sex, PTH, N-MID-OT, calcium, phosphorus, and MCHC-were included in machine learning modeling. Using the test set data as input, the performance of each machine learning model in predicting osteoporotic fractures was compared. The results showed that the XGBoost model performed best with an AUC of 0.974 (95% CI: 0.950-0.998). The other four machine learning models also achieved good prediction performance, with AUCs ranging from 0.891 to 0.964. SHAP results indicated that N-MID-OT, calcium, and PTH had relatively large weights in the model, which was consistent with clinical cognition. Conclusions The XGBoost model constructed based on routine clinical indicators demonstrates good discriminative ability and stability in predicting the risk of osteoporotic fractures, has potential clinical application value, and is suitable for primary hospitals that are unable to conduct complete bone metabolism marker testing.