Recent advancements in healthcare, driven by AI, are reshaping women’s health. The COVID-19 pandemic, telehealth, genomics, and personalized medicine initiatives are accelerating AI adoption, particularly in drug recommendations, fertility tracking, and managing reproductive health conditions. However, existing drug recommendation systems often overlook gender-specific factors like hormonal fluctuations, leading to suboptimal outcomes for conditions such as thyroid disorders and PCOS. This study proposes an AI-enhanced drug recommendation system, utilizing a Naive Bayes classifier trained on a custom women’s health dataset. The system demonstrated strong results with a 90.90% accuracy, 95.45% precision, 90.90% recall, and an F1 score of 91.77%, offering personalized recommendations and significantly improving healthcare outcomes by addressing the unique needs of women.