Background <p>Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography (AI-PPG) age, a deep learning-based estimate of biological age from raw PPG signals, and evaluate its potential as a digital biomarker for cardiovascular health.</p> Methods <p>We developed a deep learning model with a distribution-aware loss function to reduce bias from imbalanced data. The model was trained and evaluated on the UK Biobank cohort (<i>N</i> = 212,231). We analyzed the association between the AI-PPG age gap (AI-PPG age minus calendar age) and multiple cardiovascular and metabolic outcomes, assessed its longitudinal value using serial PPG measurements, and externally validated its generalizability in an independent MIMIC-III-derived cohort (<i>N</i> = 2343).</p> Results <p>After adjusting for key confounders, participants with an AI-PPG age gap greater than 9 years have a significantly higher risk of major adverse cardiovascular and cerebrovascular events (hazard ratio of 2.37, <i>p</i> = 8.46 × 10⁻⁸⁰), as well as seven secondary outcomes including coronary heart disease and myocardial infarction (all <i>p</i> &lt; 0.005). Conversely, those with a gap below −9 years show a lower risk profile. Longitudinal analysis demonstrates that changes in AI-PPG age add predictive value over time. In the external validation cohort, each one-year increase in AI-PPG age gap is associated with higher in-hospital mortality (odds ratio of 1.02, <i>p</i> = 0.01).</p> Conclusions <p>AI-PPG age is a scalable, non-invasive biomarker for cardiovascular health assessment. Integrated with wearable devices, it may enable population-level screening, personalized monitoring, and early intervention.</p>