Oral malignancy, notably oral squamous cell carcinoma (OSCC), stands as a formidable global health issue, characterized by disparate prevalence among demographics and geographic regions. Traditional diagnostic modalities, reliant on biopsy and histopathological methods, they all often exhibit constraints in expeditiousness and subjectivity, thus an alternative methodologies are needed for fostering early detection and personalized therapeutic strategies. Artificial intelligence (AI) emerges as a forefront avenue in oral cancer (OC) therapeutics, engaged in providing solutions for diagnostic augmentation, treatment optimization, and prognostic delineation. Machine learning paradigms, encompassing supervised and unsupervised learning, afford meticulous classification and pattern identification from multifarious clinical and histopathological datasets. Deep learning architectures, exemplified by convolutional neural networks (CNNs), automatize lesion detection, and characterization from medical imagery, thereby expediting diagnostic efficacy. Predictive analytics methodologies combine multifaceted patient data to access risk and prognosticate disease trajectory, thereby facilitating bespoke treatment schema. Expert systems harness medical knowledge and patient-centric intelligence to furnish decision support for clinicians in treatment modality selection and disease monitoring. Robotic and automated systems contribute to surgical precision and procedural streamlining, ultimately fostering enhanced patient outcomes. Despite these advancements, challenges remain persists necessitating continued interdisciplinary collaboration and research efforts. This review explores about burgeoning role of AI in OC therapeutics, elucidating extant applications, challenges, and future trajectories for research and clinical adoption in oral oncology. © 2025 Elsevier B.V., All rights reserved.