Introduction:Effective reduction in prenatal mortality and comorbid complications, and associated expenses necessitates the determination of the causative agents and early identification of the risk of preterm delivery in pregnant women.Methodology: A cross-sectional study was conducted on the cases of 5651 mothers. Data was collected based on the information recorded in the patient files within the time period of 2016-2020 in the community health centers of Gonabad. Logistic regression model was employed to determine the factors associated with preterm delivery. The external validity of the model of the factors predicting preterm delivery was independently analyzed for the data collected from 100 postpartum women.Findings: Of the total deliveries studied, preterm delivery occurred in 11.5% of them (i.e., 649 deliveries). The multiple logistic regression model included 7 variables for predicting preterm birth. The developed model was checked for internal and external validity using accuracy, specificity, sensitivity, positive predictive value, negative predictive value and the area under the ROC curve. In the educational data, accuracy, specificity, sensitivity positive predictive value, negative predictive value, and the area under the ROC curve was 65.4%, 65.8%, 66.0%, 19.3%, 92.9%, 0.681, respectively. Conclusion: This model has used 7 most important independent predictors of preterm delivery and the performance of the model is relatively good. Using the variables of this study, health care workers can identify mothers at risk of preterm delivery, and take the necessary measures to protect the mothers at risk. The results of this research can initiate further studies in this field.