Background: Oral cancer remains a significant global health burden, often diagnosed at advanced stages, leading to high morbidity and mortality. The quality of life and survival rates are significantly increased by early identification. Purpose: This systematic review is to assess the available data on artificial intelligence's potential to aid in the early detection and diagnosis of oral cancer. It focuses on evaluating the clinical applicability, sensitivity, specificity, and diagnostic accuracy of AI-based tools in contrast to traditional diagnostic techniques. Study selection: A systematic literature search was performed using PUBMED, MEDLINE, EMBASE, and COCHRANE Library with language restriction to English. The search was carried out incorporating the published literature till 2026 using the MeSH (medical subject heading) terms. A literature search was done out of 245 publications, related to search strategy, 57 full articles, which were related to the study, were acquired for further inspection. Out of the 49 articles, 6 articles met the inclusion criteria. Information related to study characteristics, types of AI models used, imaging techniques, and reported diagnostic performance was collected and reviewed. Results: The reviewed studies demonstrate that AI models, particularly convolutional neural networks, exhibit high diagnostic accuracy in evaluating clinical photographs, histopathology, radiographs, and autofluorescence images. Many reports showed sensitivity and specificity above 85%, signifying ability of AI to differentiate malignant and potentially malignant lesions from benign conditions. Conclusion and Relevance: Artificial Intelligence serves as a promising adjunct in the early detection and diagnosis of oral cancer, offering high diagnostic accuracy and improved support for clinical decision-making without replacing professional expertise. This review highlights the potential of artificial intelligence to enhance early and accurate detection of oral cancer, which can significantly improve patient survival and treatment outcomes. It also underscores the role of AI as a supportive clinical tool that can increase diagnostic consistency and aid clinicians in timely decision-making.