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Oral cancer remains one of the deadliest malignancies with more than half of late-stage patients dying within five years of diagnosis globally. Late presentation before initial diagnosis is a principal driver of poor outcomes for the disease. Artificial intelligence (AI), particularly deep learning systems, has emerged as a promising tool to improve the sensitivity and timeliness of oral cancer detection. This narrative review examines the current state of AI-assisted oral cancer diagnosis, evaluating the performance of image-based approaches [including convolutional neural networks (CNNs) applied to clinical photographs, histopathological slides, and cytology workflows] and critically analyzes where these systems fall short. Of note is the fact that AI models are functionally blind to submucosal and early infiltrating lesions without visible surface change, and their detection of high-grade epithelial dysplasia remains poor. Model generalizability across institutions is further compromised by the absence of large-scale standardized oral cancer image repositories. This review argues that the next frontier must shift from morphology-based learning to biologically informed AI [specifically through multiomics integration, tumor microenvironment characterization, and molecular imaging biomarkers] in order to detect oral cancer earlier and more reliably.