Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.

Background: The integration of AI into oral squamous cell carcinoma (OSCC) diagnostics is a major milestone for digital dentistry and computational pathology, enabling faster, highly accurate detection. While numerous systematic reviews have attempted to synthesize this burgeoning evidence, their conclusions remain discordant and methodological quality varies substantially. Aim: This umbrella review undertakes a rigorous synthesis of systematic review evidence to establish definitive accuracy metrics, evaluate methodological robustness, quantify evidence redundancy, and delineate critical implementation barriers. Methods: A comprehensive search was conducted across MEDLINE/PubMed, Embase, Scopus, Web of Science, and Cochrane CENTRAL from inception through September 30, 2025, following PRISMA guidelines and an a priori PROSPERO-registered protocol (CRD420251157762). Systematic reviews evaluating AI for OSCC diagnosis were included. Methodological quality was appraised using AMSTAR-2, while primary study overlap was quantified via Corrected Covered Area (CCA). A meta-meta-analysis employing robust variance estimation was performed to pool diagnostic accuracy measures, accounting for statistical dependency. Results: Fifteen systematic reviews synthesizing 341 primary studies were included. AMSTAR-2 assessment revealed a concerning distribution: only 3 reviews (20%) achieved high confidence, while 3 (20%) were critically low. Moderate primary study overlap was evident (CCA = 9.06%). The meta-meta-analysis, incorporating data from six reviews, yielded a pooled sensitivity of 0.90 (95% CI: 0.81–0.99; I ² = 85%; τ ² = 0.032). The 95% confidence interval suggested that the diagnostic performance of AI may vary substantially in future comparable reviews, reflecting differences in AI models, imaging modalities, study populations, and methodological approaches. Although the pooled estimate indicates high overall sensitivity, the wide confidence and prediction intervals indicate uncertainty regarding the magnitude and consistency of the effect, suggesting that AI performance may not be uniformly reproducible across different clinical settings Conclusions: AI demonstrated compelling diagnostic accuracy for OSCC, particularly when applied to histopathological specimens. However, the evidence base is compromised by significant heterogeneity, methodological inconsistency, and a critical deficit in implementation research. This synthesis provided an authoritative evidence foundation and a strategic roadmap for researchers, clinicians, and policymakers navigating the evolving landscape of AI in oral oncology. Clinical relevance: The integration of AI into routine clinical practice may assist clinicians in identifying suspicious lesions at earlier stages, improving diagnostic consistency, reducing inter-observer variability, and supporting timely referral and treatment decisions. Furthermore, AI-driven tools can facilitate large-scale screening programs, particularly in resource-constrained settings where access to specialist expertise may be limited. Although promising, successful clinical implementation requires rigorous validation, standardization of algorithms, transparency in decision-making processes, and evaluation of real-world effectiveness to ensure safe, reliable, and equitable patient care. Systematic Review Registration: PROSPERO [CRD420251157762].