Oral cancer is one of the fatal diseases present in society. Its late-stage diagnosis puts it under the list of diseases with high mortality rates. Deep learning has conceptualized the revolution in the field of medical imaging with impressive depth and accuracy in diagnosis and early detection. This review highlights the advancements in deep-learning-based methodologies for oral cancer detection and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep reinforcement learning (DRL). Multi-modal learning and hybrid models combining histopathological and radiological data have increased the precision of tumor segmentation and subtype classification. The observational data will help provide insights into the factors responsible for explaining the model’s decisions and the associated risks, which are most relevant for potential applications. The development of transfer learning and self-supervised learning also seems to have significantly solved some of the most serious challenges regarding the volume of clinical data. Future studies can harness standardized practices for data collection, deploy technically sound explainable AI frameworks, and conduct clinical validations as the closing gap between tremendous strides made in deep learning and practical real-world implementation. This review provides a wide-ranging overview of such AI-driven methodologies, focused on the critical challenges and future directions for improvements in early oral cancer detection and reduced mortality rates.