Accurate periodontal diagnosis is essential for effective prevention, staging, prognosis, and treatment planning, yet it is inherently limited by biological complexity, observer variability, and the retrospective nature of conventional diagnostic approaches. Clinical measurements and radiographic imaging predominantly reflect cumulative tissue destruction and offer limited insight into disease activity and future risk. On the other hand, diagnosis, staging, and risk assessment of periodontal diseases remain complex due to their multifactorial nature and the inherent variability of clinical and imaging-based evaluations. Artificial intelligence (AI) has emerged as a potential valuable adjunct in periodontology by improving consistency, reducing examiner-dependent variability, and supporting earlier identification of disease-related changes that may be underestimated in conventional assessments. This chapter reviews the biological and clinical principles underlying periodontal diagnosis to contextualize the strengths and constraints of established diagnostic methods. Current evidence supporting conventional imaging and digital diagnostic workflows is examined, followed by an appraisal of AI-driven approaches for radiographic interpretation and multimodal data integration. The application of AI with machine learning and deep learning for periodontal tasks such as plaque detection, gingivitis identification, quantification of alveolar bone loss, biomarker-based disease assessment, and prognostic risk modeling is discussed along with challenges regarding data standardization, external validation, and clinical integration. Overall, AI is positioned as an augmentative technology whose clinical value depends on rigorous validation, biological plausibility, and meaningful integration into routine periodontal practice.