Periodontal disease progression remains a major clinical challenge because deterioration is rarely explained by a single biological or behavioral factor. Changes in probing depth, attachment loss, bleeding, plaque control, smoking exposure, systemic health, and radiographic bone loss interact over time to produce heterogeneous trajectories. Conventional risk assessment approaches often simplify this complexity into static or linear categories. A more integrative approach is needed to support earlier identification of patients at risk for progressive destruction. Existing periodontal prediction methods are limited by their dependence on isolated clinical variables or broad risk categories. Although staging and grading systems have improved diagnostic consistency, they do not fully operationalize longitudinal, multimodal, and nonlinear prediction. Behavioral adherence, radiographic architecture, and site-level disease history may modify risk in ways that conventional tools do not capture. These limitations reduce the clinical usefulness of risk estimates when treatment planning requires individualized prognosis. This article proposes a conceptual framework for machine learning-based prediction of periodontal disease progression. The framework integrates clinical, behavioral, systemic, and radiographic determinants into a unified predictive architecture. It is designed to guide future empirical model development rather than to report new patient data. The central objective is to define how heterogeneous periodontal data can be harmonized, modeled, interpreted, and translated into clinical decision support. The framework is derived from a critical synthesis of peer-reviewed literature published between 2017 and 2026 across periodontology, dental radiology, health informatics, and predictive modeling. It identifies three principal predictor domains: clinical inflammatory and attachment measures, behavioral and systemic modifiers, and radiographic indicators of alveolar bone destruction. It also proposes model selection logic, explainability requirements, and workflow integration principles. Two tables summarize the predictor domains and the proposed framework components. The proposed framework provides a structured roadmap for next-generation periodontal risk prediction tools. By combining multimodal data with interpretable machine learning, it aims to support timely intervention, personalized maintenance planning, and improved communication of progression risk. Its value lies in offering a clinically grounded blueprint for future validation studies, software development, and implementation research. The framework emphasizes that prediction should augment, not replace, professional periodontal judgment.
