Artificial intelligence (AI) is increasingly applied in digital pathology for gynecological cancers, enhancing diagnostic accuracy and efficiency. Digital pathology involves converting histopathology slides into whole-slide images (WSIs) for computer-based analysis. AI, particularly deep learning (DL), has shown promise in classifying histopathological subtypes, predicting treatment response, and prognosis. DL models, such as convolutional neural networks (CNNs), are widely used for tasks like ovarian cancer (OC) subtype classification, endometrial cancer (EC) differentiation, and cervical cancer (CC) detection. These models achieve high accuracy, with AUC values ranging from 0.71 to 0.99. AI also aids in cancer diagnosis by distinguishing between malignant and benign lesions, and in prognosis by predicting survival and molecular features. However, challenges remain, including the 'black box' problem, data quality, model generalization, and validation. Efforts to improve interpretability and integrate AI with multi-omics data are ongoing. Despite these challenges, AI holds significant potential to enhance clinical decision-making and improve patient outcomes in gynecological cancers.Artificial intelligence (AI) is increasingly applied in digital pathology for gynecological cancers, enhancing diagnostic accuracy and efficiency. Digital pathology involves converting histopathology slides into whole-slide images (WSIs) for computer-based analysis. AI, particularly deep learning (DL), has shown promise in classifying histopathological subtypes, predicting treatment response, and prognosis. DL models, such as convolutional neural networks (CNNs), are widely used for tasks like ovarian cancer (OC) subtype classification, endometrial cancer (EC) differentiation, and cervical cancer (CC) detection. These models achieve high accuracy, with AUC values ranging from 0.71 to 0.99. AI also aids in cancer diagnosis by distinguishing between malignant and benign lesions, and in prognosis by predicting survival and molecular features. However, challenges remain, including the 'black box' problem, data quality, model generalization, and validation. Efforts to improve interpretability and integrate AI with multi-omics data are ongoing. Despite these challenges, AI holds significant potential to enhance clinical decision-making and improve patient outcomes in gynecological cancers.