Smart Technologies in the Management of Metabolic Syndrome and Oral Health: A Narrative Review

Background/Objectives: Metabolic syndrome (MetS) and periodontitis are two of the most common chronic conditions sharing an underlying mechanism represented by systemic inflammation. Smart technologies, including machine learning (ML), wearable and Internet of Things (IoT) devices, telemedicine and intraoral sensors, are increasingly used in both fields. This narrative review examined how these technologies are used to predict, monitor and manage MetS and oral diseases and what evidence supports combining oral and metabolic data. Methods: Following the SANRA framework, PubMed, Scopus and Web of Science were searched for English-language articles published between January 2021 and May 2026. Of the 6922 records identified (3520 after de-duplication), 1852 were screened by title and abstract, and studies were selected by relevance to three research questions. Applications were classified by their current level of evidence. Results: ML models for MetS prediction often reported high discrimination (area under the receiver operating characteristic curve [AUC] up to 0.99), but most were validated only internally, and several used the diagnostic components of MetS as predictors. Wearable, telemedicine and mobile interventions, usually combined with lifestyle programs, were associated with improvements in body weight, blood pressure and glycemic markers; artificial intelligence tools for dental imaging showed accuracy comparable to clinicians in mostly retrospective studies, with limited evidence on patient outcomes. Few studies combined oral and metabolic data: cross-sectional ML studies identified periodontal severity as a predictor of MetS and metabolic conditions as predictors of periodontitis, and salivary glucose correlated with blood glucose mainly in people living with diabetes, which does not establish diagnostic accuracy. No prospective study has tested an integrated dental–medical pathway. Conclusions: Integrating oral and metabolic data through smart technologies is biologically plausible and appears technically feasible, but its added diagnostic value, clinical effectiveness and cost effectiveness have not been established. Externally validated models, prospective cohorts and trials of integrated care pathways are required before routine use.