Purpose: This systematic review evaluated the diagnostic performance of optical coherence tomography (OCT) forthe early detection of oral cancer, particularly emphasizing sensitivity, specificity, and overall diagnostic accuracy. Materials and Methods: A comprehensive literature search was conducted across PubMed, ScienceDirect, and Wiley Online Library covering publications from 2015 to 2025, supplemented by manual hand-searching of relevant references. Studies were selected using the Population, Intervention, Comparison, Outcome, Study Design (PICOS) framework. Eligible studies evaluated the diagnostic accuracy of OCT using histopathology as the reference standard. Risk of bias was assessed using the QUADAS-2 tool, and the review methodology followed PRISMA 2020 and PRISMA-DTA guidelines. The review protocol was registered in PROSPERO (CRD420251112254). Results: Seven studies met the predefined inclusion criteria. OCT demonstrated high diagnostic performance (sensitivity: 81.5%-100%, specificity: 68.8%-100%), with diagnostic accuracy reaching up to 100% in certain settings. Studies that incorporated machine learning approaches, including convolutional neural networks and support vector machines, consistently achieved superior diagnostic performance compared with conventional OCT interpretation alone. Overall methodological quality was generally low, with several studies exhibiting moderate tohigh risk of bias in specific domains. Conclusion OCT, particularly when augmented with artificial intelligence, demonstrates high diagnostic accuracy as anon-invasive imaging modality for the early detection of oral cancer. Its capability to identify dysplastic and malignant changes at the microstructural level offers meaningful diagnostic advantages over conventional examination methods.Nevertheless, larger-scale studies employing standardized protocols are required to confirm its clinical utility and support integration into routine oral cancer screening and diagnostic pathways.