Oral cancer is a major global health burden, ranking sixth in prevalence, with oral squamous cell carcinoma (OSCC) being the most common type. Importantly, OSCC is often diagnosed at late stages, underscoring the need for innovative methods for early detection. The oral microbiome, an active microbial community within the oral cavity, holds promise as a biomarker for the prediction and progression of cancer. Emerging computational techniques in the artificial intelligence (AI) field have enabled the analysis of complex microbiome data sets to unravel the association between oral microbiome composition and oral cancer. This review provides a comprehensive overview of learning‐based algorithms applied to oral microbiome data for cancer prediction. In particular, this work discusses how typical machine learning (ML) algorithms, such as logistic regression, random forests, and artificial neural networks, identify the unique microbial patterns associated with oral cancer and other malignancies. A search was conducted in Pubmed covering a 10‐year period. The goal was to identify previous studies focused on the role of the oral microbiome in oral cancer prediction using AI‐powered tools. The search strategy identified 3382 records in total, of which 44 studies met the inclusion criteria. While AI has shown a transformative power in understanding and revealing the oral microbiome's role in cancer studies, its application in clinical settings requires further efforts on standardization of protocols, curation of diverse cohorts, and validation through large‐scale multi‐centric and longitudinal studies. The integration of AI with oral microbiome analysis holds significant promise for improving early detection, risk stratification, and personalized treatment strategies for OSCC. By identifying unique microbial patterns associated with cancer, AI‐driven models offer a noninvasive, cost‐effective tool to predict disease progression and guide clinical decision‐making. However, translating these advancements into routine clinical practice requires standardized protocols, diverse patient cohorts, and validation through large‐scale, longitudinal studies. Once implemented, this approach could transform oral cancer management, enabling timely interventions and improving patient outcomes.

Background: Periodontitis is a chronic inflammatory disease that causes tissue destruction due to the imbalance in the oxidant-antioxidant system. Melatonin has anti-inflammatory, antioxidant, and immune-modulatory properties and is considered to be a biomarker and diagnostic/therapeutic agent in the pathogenesis of periodontitis. Objectives: The present study aimed to evaluate the salivary melatonin level and its changes following non-surgical periodontal therapy in patients with periodontitis. Methods: In total, 90 salivary samples were collected from 60 patients, including 30 from patients with moderate chronic periodontitis (before periodontal treatment), and 30 from the same patients one month after the non-surgical periodontal therapy, and 30 from periodontally healthy subjects (control). Salivary melatonin levels were measured using the competitive immunoassay of the human melatonin ELISA kit. Results: The highest melatonin concentration was observed in the control group (79.55 ± 59.22; P < 0.05), while the lowest concentration was observed in the pre-treatment group (P < 0.05). In addition, salivary melatonin concentration in the post-treatment group (56.58 ± 46.48) was significantly higher compared to the pre-treatment group (17.25 ± 5.79; P < 0.05). Conclusions: According to the results, salivary melatonin levels improved after non-surgical periodontal therapy, which suggests the involvement of melatonin in the pathogenesis of periodontitis. However, the exact role of melatonin requires further investigation.