<p>Very Preterm birth (vPTB) and extreme Preterm birth (xPTB) are the major concerns in maternal and child healthcare and are associated with increased morbidity and mortality. Machine learning methods have traditionally been used to predict preterm births (vPTB and xPTB). However, most medical datasets, including preterm births, are imbalanced in class distribution. Although data-balancing techniques can be employed, complications due to the limited sample size of the minority class are frequently encountered, leading to inconsistent results. This study adopted a novel approach by employing one-class classification (OCC) in conjunction with several strategies to predict instances of vPTB and xPTB within an Emirati pregnant population. We used a well-curated dataset acquired during the first trimester of pregnancy. We employed multiple OCC algorithms and their ensembles involving multiple aggregation strategies to predict vPTB and xPTB in both parous and nulliparous populations. Our approach effectively incorporated only majority class information during training. Our detailed experimental setup demonstrated that the proposed methodology achieved promising performance with a maximum AUC-ROC of 0.823 for the parous population without any explicit modeling of the minority class. Our approach demonstrated robustness and efficacy in identifying at-risk pregnancies within the Emirati population. Our results suggest that one-class classification framework which requires only normal data points for training can be used for early prediction of very preterm and extreme preterm births with reasonable accuracy. In this paper, we applied one-class classification framework only on the Emirati population. Generalizing the proposed approach in this domain requires experimentation on similar datasets from other countries.</p>