Objective To explore the predictive value of five machine learning models based on clinical characteristics and integrated inflammatory biomarkers for preterm birth，and to compare the predictive performance of different models in order to provide evidence for their clinical application. Methods A retrospective cohort study was conducted，including 1335 pregnant women who delivered in the Department of Obstetrics and Gynecology at the Fifth Affiliated Hospital of Sun Yat-sen University between January 1， 2022， and December 31， 2022，with all types of preterm birth included （spontaneous preterm birth and indicated preterm birth）. Totally 20 clinical characteristics，10 laboratory testing indexes，and 4 integrated inflammatory biomarkers（Neutrophil-to-Lymphocyte Ratio，Platelet-to-Lymphocyte Ratio，Aggregate Index of Systemic Inflammation，and Albumin-to-Lymphocyte Ratio）we screened out via literature review and clinical observation，and five machine learning models（Random Forest，XGBoost，Support Vector Machine，Lasso Regression，and Logistic Regression）were constructed. Hyperparameters were optimized via repeated 5-fold cross-validation，and the models were evaluated from 3 dimensions of discrimination，calibration，and clinical utility. Results The discrimination among the five models，the XGBoost model achieved the highest AUC（0.763），significantly higher than that of Logistic Regression（AUC=0.683）（P<0.05），followed by Random Forest（AUC=0.730），Support Vector Machine（AUC=0.707），and Lasso Regression（AUC=0.696）. In terms of calibration，the XGBoost model had the lowest Brier score（0.182），indicating the best calibration performance. Decision curve analysis showed that the XGBoost model had the highest standardized net benefit around the 20% risk threshold，indicating significant clinical value in practice.After adding integrated inflammatory biomarkers，the AUC of the model decreased from 0.763 to 0.730（ΔAUC=-0.034，P=0.0033），which did not improve predictive performance of the model. Conclusions All five machine learning models constructed in this study can be applied to preterm birth risk prediction， among which the XGBoost model demonstrates the best performance across the three dimensions of discrimination， calibration， and clinical utility. Owing to its extremely high specificity （97.2%）， it can serve as a tool for secondary confirmation and refined risk stratification in high-risk populations， rather than as a first-line universal screening tool. Integrated inflammatory biomarkers failed to provide incremental predictive value in the population included in this study.