This systematic literature review (SLR) explores the application of data mining (DM) and machine learning (ML) in gynecologic oncology. The study analyzed 3499 articles published between 2011 and 2022, identifying 181 eligible studies. The majority of these studies focused on cervical neoplasms, with a significant spike in publications after 2019. The research highlights the use of DM-ML techniques, primarily neural networks, for classification and diagnosis in gynecologic oncology. Key findings include the prevalence of cervical cancer as the most studied site, with a focus on classification and diagnostic tasks. The study also identifies common data modalities, such as medical imaging and follow-up biopsies, and discusses the performance metrics and validation techniques used in the research. The review emphasizes the need for cross-cohort generalizability and the importance of addressing data interoperability and bias in local models. While the majority of studies are empirical, there is a notable lack of validation research and clinical trials. The study concludes that DM-ML has the potential to enhance precision medicine in gynecologic oncology but requires further research to improve clinical applicability and address existing gaps in data sharing and validation. The findings underscore the importance of interdisciplinary collaboration and the need for standardized approaches to ensure the effective integration of DM-ML in clinical practice.This systematic literature review (SLR) explores the application of data mining (DM) and machine learning (ML) in gynecologic oncology. The study analyzed 3499 articles published between 2011 and 2022, identifying 181 eligible studies. The majority of these studies focused on cervical neoplasms, with a significant spike in publications after 2019. The research highlights the use of DM-ML techniques, primarily neural networks, for classification and diagnosis in gynecologic oncology. Key findings include the prevalence of cervical cancer as the most studied site, with a focus on classification and diagnostic tasks. The study also identifies common data modalities, such as medical imaging and follow-up biopsies, and discusses the performance metrics and validation techniques used in the research. The review emphasizes the need for cross-cohort generalizability and the importance of addressing data interoperability and bias in local models. While the majority of studies are empirical, there is a notable lack of validation research and clinical trials. The study concludes that DM-ML has the potential to enhance precision medicine in gynecologic oncology but requires further research to improve clinical applicability and address existing gaps in data sharing and validation. The findings underscore the importance of interdisciplinary collaboration and the need for standardized approaches to ensure the effective integration of DM-ML in clinical practice.