This paper explores the application of machine learning models for predicting pregnancy risks, focusing on the performance comparison of XGBoost, Random Forest, and Decision Tree classifiers. The motivation behind this research stems from the critical need for early identification of high-risk pregnancies to improve maternal health outcomes. Using a dataset consisting of anonymous information from pregnant women in rural Bangladesh, this study implements feature scaling, standardization, and encoding to prepare the data. Both pre- and post-hyperparameter tuning results are analysed, with additional focus on handling imbalanced data through the application of SMOTE (Synthetic Minority Over-sampling Technique). The evaluation metrics include accuracy, precision, recall, F1-score, and ROC curves for each class. Key findings indicate that XGBoost outperforms the other models, particularly after hyperparameter tuning and SMOTE application, achieving an accuracy of 82%. The study emphasizes the importance of advanced machine learning techniques in healthcare, offering significant implications for early and accurate prediction of pregnancy-related risks.