A narrative review on artificial intelligence in gynecologic oncology emphasizes the limitations of current data and explores prospective directions for clinical application. Gynecological cancers, originating in the epithelial tissue and including endometrial, ovarian, and cervical cancers, contribute to high rates of illness and fatality worldwide, with patient prognosis remaining poor despite advancements. Conventional treatments include radiation therapy, chemotherapy, surgery, and targeted therapy. The review notes that machine learning (ML), a subset of artificial intelligence (AI), is emerging as a transformative technology for analyzing complex medical data, showing potential in tumor type identification, automating Pap smears, predicting recurrence or metastasis, and discovering novel biomarkers. Over the past decade, AI application in cancer imaging has increased, with AI radiomics developed to assist physicians. Recent advancements have shifted algorithms from simple convolutional neural networks (CNNs) to advanced transformer-based architectures, highlighting the need for detailed usage guidelines for computational pathology products to mitigate potential biases from pre-scanning procedures that impact AI system performance.