This research implements an intelligent, bilingual pregnancy health monitoring system for expectant mothers. A significant problem commonly experienced by expectant mothers in rural areas in Nigeria is the unavailability of a decent antenatal system and a shortage of experienced medical personnel and equipment. The proposed system comprises IoT sensors, including Electrocardiogram (ECG), body temperature, and heart rate sensors, connected to an ESP32 microcontroller for data acquisition and transmission. A predictive system built using Random Forest and Support Vector Machine (SVM) classifiers categorises pregnancy risk into low, medium, and high. A Flask-based web application for real-time data visualization and diagnosis was developed to display the collected data and visually represent the risk level diagnosis. The performances of the predictive models, Random Forest and Support Vector Machine (SVM), were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved an accuracy surpassing SVMs by a margin of 5.28%. Random Forest and SVM precision were then compared and there was an improvement of 6.49%. 
In addition, Random Forest had a higher recall than SVM by 6.58%, and also had a performance increase of 6.49% on F1-score as compared to SVM. The comparative analysis shows that the Random Forest model works better than SVM in all the main measures. In this project, the Random Forest model was better than the SVM because it uses ensemble learning to manage the non-linear relationship, imbalance data and noise better to achieve superior accuracy, recall, and the F1 Scores. It was also more reliable in categorizing risks in pregnancy, as it was interpretable, which was also strong and guaranteed the timely and suitable intervention of health care.