The research reflects the use of different machine learning algorithms in predicting fetal health outcomes, such as Support Vector Classifier (SVC), Random Forest (RF), and Hybrid models integrating both the two models. Data from this research were obtained from Kaggle and contain broad clinical data on fetal health, whose key features include Baseline value, Accelerations, Fetal movement, Abnormal short-term variability, Histogram mean, and Fetal health. Data preprocessing and exploratory analysis were carried out in Python in a Google Colab environment, where cleaning, normalization, and visualization of the dataset were done. Later, the data was split into an 80:20 ratio for training and testing, respectively. Accuracy, precision, recall, and F1-score were the performance indicators used to assess the models. The Hybrid model HTSVC_RF had the highest value of accuracy, 94.36%, while considering the individual models, the SVC had an accuracy of 90.66% and RF had an accuracy of 93.82%. These results essentially signify that the Hybrid model has really utilized the strengths of both SVC and RF; therefore, it acts as a robust tool for fetal health prediction. These results can be considered an indication of the potentiality of machine learning in enhancing prenatal care through the early and accurate prediction of fetal health conditions, which would definitely reduce fetal morbidity and mortality.