Preeclampsia is a leading cause of maternal morbidity and mortality worldwide, with early detection being critical for reducing adverse outcomes. This study aimed to develop a machine learning model for predicting the risk of preeclampsia using readily available maternal characteristics such as body mass index, mean arterial pressure, and clinical history of hypertension or diabetes mellitus. Secondary data from 2,250 pregnancies were analyzed, addressing challenges such as missing data and class imbalance through preprocessing. Various algorithms, including support vector machines, random forest, and logistic regression, were evaluated. Herein, a support vector machines model with threshold adjustment showed the best performance, with a sensitivity of 67.5%, specificity of 57.23%, and an area under the curve of 0.68. These findings indicated the promising potential of scalable and interpretable prediction models for enhancing preeclampsia screening in primary health care settings. However, further refinement and validation of the proposed model are required for broader clinical integration to improve maternal and neonatal health outcomes. Copyright @ 2025, Kesmas: National Public Health Journal.