Maternal healthcare remains a critical challenge in developing regions, where early identification of high-risk pregnancies is essential for reducing maternal and neonatal morbidity and mortality. This study presents a comprehensive machine learning-based evaluation framework for predicting maternal health risks among Bangladeshi women using an organized dataset. To identify key risk factors, we performed extensive statistical analysis, including normality tests and variance analysis. We systematically evaluated key metrics of traditional and ensemble machine learning models— likely Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines, Extra Trees, XGBoost, convolutional neural networks, and advanced ensemble techniques like soft voting and stacking. Hyperparameter tuning with cross-validation was employed to optimize model performance. Among all models, Random Forest achieved the highest predictive accuracy (98.94%) with an F1-score of 0.989, along with the lowest RMSE and MAE, indicating exceptional robustness and precision. Ensemble methods such as stacking also demonstrated strong performance, achieving 97.88% accuracy. To rigorously assess model reliability, we applied stratified cross-validation and bootstrap confidence intervals. Beyond experimental evaluation, we developed a real-time maternal health risk prediction server using Streamlit, enabling clinicians and healthcare workers to input patient data and receive instant risk predictions.