Machine learning (ML) is revolutionizing prenatal healthcare by enabling early prediction and intervention in maternal and fetal complications. The complexity and volume of prenatal health data, ranging from ultrasound imaging and electronic health records to genetic and lifestyle information, make it an ideal candidate for ML applications. These algorithms can detect subtle patterns and correlations often missed by conventional diagnostic approaches, thereby improving risk assessment and outcomes for both the mother and fetus. This paper explores the current state of machine learning in prenatal health prediction, its applications in detecting conditions such as gestational diabetes, preeclampsia, and congenital anomalies, and the challenges related to data quality, interpretability, and clinical integration. By examining various ML models and real-world case studies, we highlight the potential of AI to transform prenatal care from reactive to proactive, personalized medicine.