<p>Women’s cancers, encompassing gynecological malignancies such as cervical, ovarian, and uterine cancers, alongside breast cancer, represent a substantial worldwide health burden with high incidence and mortality rates. Timely diagnosis and effective treatment are pivotal in improving patient outcomes; however, conventional diagnostic and therapeutic modalities often exhibit limitations in sensitivity, specificity, and accessibility, particularly in low-resource settings. Artificial intelligence (AI) has become a game-changer in cancer research and therapeutic practice in recent years. This review provides a comprehensive analysis of AI-driven innovations in the detection and diagnosis of women’s cancers. It explores the integration of machine learning (ML), deep learning (DL), and computer-aided diagnostic systems across various diagnostic platforms, including imaging modalities, histopathological analysis, and biomarker-based screening techniques. Comparative assessments underscore the enhanced accuracy, efficiency, and predictive capabilities of AI-based methodologies relative to traditional approaches. Furthermore, the review critically examines the prevailing challenges associated with AI deployment, such as algorithmic bias, data heterogeneity, ethical considerations, and the need for rigorous clinical validation. Finally, the paper outlines future directions and the evolving landscape of AI applications, emphasizing its promise in revolutionizing women’s cancer care and ultimately improving patient outcomes.</p> Graphical Abstract <p></p>