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Premature birth (PTB) is the most common public health issue in gynecology and obstetrics. It has a variety of consequences, ranging from increased infant mortality to chronic disease and disability throughout one's life. Many PTB prediction studies have been published in recent years to improve the accuracy of PTB prediction. However, no single study has addressed the issue of premature birth using a data set from the Iraqi community. There are many research from different countries that have been collected regarding premature birth, where the pregnant woman differs from one region to another in terms of lifestyle as well as the diagnostic methods used, where in Iraq only manual methods are adopted in the diagnosis. The aim of this paper is to provide and improve the prediction accuracy of premature births using real data from Iraqi hospitals. Additionally, provide a validation of the results in terms of reliability and accuracy. The novel approach is used to identify the best maternal characteristics (those associated with PTB) from an obstetric dataset. The following classifiers (Decision Tree, Random Forest, Logistic Regression, K-Neighbor Nearest, Naive Bayes Classifier, Neural Networks, Support Vector Machine) were used in this study to classify all birth cases into term normal birth and preterm birth. As a result, the Random Forest classifier outperformed all other classifiers with a 98% percent accuracy. Its worth noting that the classifiers' performance is quantified in terms of accuracy, sensitivity, Specificity, Precision, Recall and F1 score. [ABSTRACT FROM AUTHOR]