Gynecological cancers represent a significant health burden globally, with late-stage diagnoses often leading to poor prognoses and limited treatment options. Early detection remains paramount in improving patient outcomes and reducing mortality rates associated with these malignancies. This research proposes a novel approach to the premature recognition of gynecological cancers through the utilization of AI algorithms. By leveraging deep learning and machine learning techniques, coupled with advanced tomography modalities such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), this study aims to develop a robust AI-driven framework capable of detecting subtle abnormalities indicative of gynecological malignancies at their incipient stages. The proposed methodology involves the collection of comprehensive datasets comprising imaging data, clinical records, and histopathological findings from a diverse cohort of patients diagnosed with gynecological cancers. Additionally, radiomics and texture analysis techniques will be integrated into the framework to extract quantitative imaging biomarkers associated with gynecological cancers. Furthermore, this study emphasizes the importance of interdisciplinary collaboration between clinicians, radiologists, pathologists, and data scientists in optimizing the performance and clinical applicability of AI-driven diagnostic tools for gynecological cancers.