This review explores the integration of artificial intelligence (AI) in assessing the effects of prenatal physical activity on maternal-fetal health, with a focus on umbilical cord blood biomarkers. Utilizing engineering and AI-driven predictive modeling, this study examines the relationship between maternal physical activity and indicators of fetal health, such as lipid profiles, glucose levels, and inflammatory markers in cord blood. A systematic review of recent studies highlights the potential for AI to support personalized maternal care by developing models that predict optimal physical activity levels for improved neonatal outcomes. Key engineering principles are employed to model and analyze physiological responses to exercise, enabling more precise recommendations for gestational health. By analyzing exercise intensity, type, and frequency, the study outlines optimal strategies to mitigate risks associated with gestational diabetes, hypertension, and excessive gestational weight gain. The review also discusses epigenetic impacts of maternal exercise, suggesting that AI-enhanced bioinformatics can reveal the interplay between lifestyle factors and fetal genetic expression, potentially reducing future risks of obesity and cardiovascular diseases. This interdisciplinary approach emphasizes the value of AI and engineering in advancing maternal-fetal health and in shaping predictive healthcare tools for prenatal care optimization.