This review article explores the role of artificial intelligence (AI) in digital pathology for gynecological cancers, highlighting its clinical applications and challenges. Gynecological cancers, including ovarian, endometrial, and cervical cancers, are significant health concerns due to their high mortality rates and poor prognosis. Digital pathology, which involves digitizing histopathology slides, has enhanced the efficiency and accuracy of cancer diagnosis. AI, particularly deep learning (DL) models, has shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment responses and prognosis. DL methods, especially convolutional neural networks (CNNs), have demonstrated superior performance in these tasks. However, challenges such as interpretability, data quality, model generalization, and validation remain. The integration of AI with other omics techniques and multi-center studies is also underexplored. Despite these challenges, the potential of AI in digital pathology is significant, and ongoing advancements are expected to improve its clinical utility.This review article explores the role of artificial intelligence (AI) in digital pathology for gynecological cancers, highlighting its clinical applications and challenges. Gynecological cancers, including ovarian, endometrial, and cervical cancers, are significant health concerns due to their high mortality rates and poor prognosis. Digital pathology, which involves digitizing histopathology slides, has enhanced the efficiency and accuracy of cancer diagnosis. AI, particularly deep learning (DL) models, has shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment responses and prognosis. DL methods, especially convolutional neural networks (CNNs), have demonstrated superior performance in these tasks. However, challenges such as interpretability, data quality, model generalization, and validation remain. The integration of AI with other omics techniques and multi-center studies is also underexplored. Despite these challenges, the potential of AI in digital pathology is significant, and ongoing advancements are expected to improve its clinical utility.