Accurate prediction of pregnancy risk levels is essential for preventing maternal and fetal complications. This study explores the application of ensemble machine learning models combined with oversampling techniques to predict pregnancy risk levels. We utilized a publicly available dataset from the UCI Machine Learning Repository, performing extensive feature engineering, including the introduction of new features like Pulse Pressure and Mean Arterial Pressure. To address class imbalance, we employed the Adaptive Synthetic Sampling (ADASYN) method. We conducted comprehensive hyperparameter tuning to enhance model performance and achieve optimal predictive results. Additional evaluation metrics, including sensitivity, specificity, and precision-recall curves, were used to assess model per- formance comprehensively. Our findings demonstrate that the Voting Classifier, particularly when combined with ADASYN oversampling and optimized hyperpa- rameters, achieves an accuracy of 87.19% and a macro F1 score of 87.66%, effec- tively distinguishing between ’low risk’ and ’mid risk’ pregnancy cases. This work contributes to the field by enhancing prediction accuracy, providing insights into important features influencing pregnancy risk, and addressing ethical considerations in deploying machine learning models in healthcare.