Nearly half of the world’s stillbirths occur during labour and delivery. Early detection of any fetal distress can prompt the doctors to take appropriate measures. Cardiotocography (CTG) is one such technique that continuously records the fetal heart rate and uterine contractions during childbirth. Along with indicating signs of fetal hypoxia, CTG can also be interpreted to detect fetal abnormalities. Using the cardiotocography dataset from the UCI Machine Learning Repository, our paper displays a comparative analysis of different classifiers and ensemble learning methods such as max voting, weighted average, blending, bagging and boosting to enhance the fetal state prediction. Of all the ensemble methods used in our analysis, it was found that the Light Gradient Boosting Machine (LightGBM) gave the highest accuracy of 95.90%, which exceeded similar existing models. This increase in accuracy can prove to be potentially life saving, aid doctors in a more accurate detection of fetal abnormalities, reduce human error rates and increase infant mortality rates.