Predicting medication safety during pregnancy has long relied on sparse clinical trial data and post-market surveillance, leaving clinicians with limited guidance for the thousands of drugs lacking robust pregnancy safety profiles. This creates a critical information gap when treating pregnant patients who need medication for chronic conditions or acute illnesses. Machine learning approaches now offer a pathway to leverage existing population health data for more comprehensive safety predictions. The research demonstrates how AI models can analyze patterns in large-scale pregnancy registries and electronic health records to identify potential teratogenic risks before extensive human exposure occurs. By training algorithms on known drug-outcome relationships, researchers can extrapolate safety signals for medications with limited pregnancy data, potentially flagging concerns for drugs like certain antiepileptics or newer biologics where traditional studies would take decades to complete. This computational approach represents a significant advancement in pharmacovigilance, where traditional methods often detect safety signals only after substantial exposure has already occurred. The implications extend beyond individual prescribing decisions to public health policy, potentially enabling more proactive drug labeling and regulatory guidance. However, the methodology faces inherent limitations in distinguishing correlation from causation, particularly given the complex interplay of maternal health conditions, genetic factors, and environmental exposures that influence pregnancy outcomes. While promising for hypothesis generation and risk stratification, these AI-driven predictions require validation through targeted observational studies before informing clinical practice. The integration of machine learning into pregnancy pharmacology surveillance could fundamentally reshape how we approach medication safety assessment, moving from reactive monitoring to predictive risk evaluation.