BACKGROUND AND OBJECTIVE: Gynecological cancer is the most common cancer that affects women's quality of life and well-being. Artificial intelligence (AI) technology enables us to exploit high-dimensional imaging data for precision oncology. Tremendous progress has been made with AI radiomics in cancers such as lung and breast cancers. Herein, we performed a literature review on AI radiomics in the management of gynecological cancer. METHODS: A search was performed in the databases of PubMed, Embase, and Web of Science for original articles written in English up to 10 September 2024, using the terms "gynecological cancer", "cervical cancer", "endometrial cancer", "ovarian cancer", AND "artificial intelligence", "AI", AND "radiomics". The included studies mainly focused on the current landscape of AI radiomics in the diagnosis, treatment, and prognosis of gynecological cancer. KEY CONTENT AND FINDINGS: A total of 128 studies were included, with 86 studies focusing on tumor diagnosis (n=23) and characterization (n=63), 15 on treatment response prediction, and 27 on recurrence and survival prediction. AI radiomics has shown potential value in tumor diagnosis and characterization [tumor staging, histological subtyping, lymph node metastasis (LNM), lymphovascular space invasion (LVSI), myometrial invasion (MI), and other molecular or clinicopathological factors], chemotherapy or chemoradiotherapy response evaluation, and prognosis (disease recurrence or metastasis, and survival) prediction. However, most included studies were single-center and retrospective. There was substantial heterogeneity in methodology and results reporting. CONCLUSIONS: AI radiomics has been increasingly adopted in the management of gynecological cancer. Further validation in large-scale datasets is needed before clinical translation.