<p>Oral cancer remains one of the leading causes of morbidity and mortality worldwide, necessitating continuous advancements in diagnostic methods. This review explores the progression of oral oncology detection over the past three decades, from traditional techniques to cutting-edge artificial intelligence (AI) and machine learning (ML) approaches. The first section examines foundational research from 1990 to 2000, focusing on the limitations of histopathology and early imaging techniques. The period from 2000 to 2010 introduced molecular biomarkers and advanced imaging technologies, which provided improved diagnostic capabilities but were still constrained by accessibility and sensitivity issues. The decade from 2010 to 2020 saw the integration of AI and ML, enhancing diagnostic accuracy and efficiency through automated analysis and the use of multimodal data. Finally, the review discusses current trends from 2020 to 2024, highlighting the continued integration of AI, the rise of personalized diagnostics, and the ongoing efforts to make these technologies more accessible. By providing a detailed analysis of the year-on-year advancements in oral oncology detection, this review underscores the significant strides made toward improving early detection, which is crucial for better patient outcomes and treatment strategies. </p>