Background: Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys information regarding aortic stiffness; however, the best methods to extract and interpret waveform features remain controversial. Methods: We trained a convolutional neural network with fixed-scale (time and amplitude) brachial, radial, and carotid tonometry waveforms as input and negative inverse carotid-femoral pulse wave velocity as label. Models were trained with data from 2 community-based Icelandic samples (N=10 452 participants with 31 126 waveforms) and validated in the community-based Framingham Heart Study (N=7208 participants, 21 624 waveforms). Linear regression rescaled predicted negative inverse carotid-femoral pulse wave velocity to equivalent artificial intelligence vascular age (AI-VA). Results: The AI-VA model predicted negative inverse carotid-femoral pulse wave velocity with R ² =0.64 in a randomly reserved Icelandic test group (n=5061, 16%) and R ² =0.60 in the Framingham Heart Study. In the Framingham Heart Study (up to 18 years of follow-up; 479 cardiovascular disease, 200 coronary heart disease, and 213 heart failure events), brachial AI-VA was associated with incident cardiovascular disease adjusted for age and sex (model 1; hazard ratio, 1.79 [95% CI, 1.50–2.40] per SD; P <0.0001) or adjusted for age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, prevalent diabetes, hypertension treatment, and current smoking (model 2; hazard ratio, 1.50 [95% CI, 1.24–1.82] per SD; P <0.0001). Similar hazard ratios were demonstrated for incident coronary heart disease and heart failure events and for AI-VA values estimated from carotid or radial waveforms. Conclusions: Our results demonstrate that convolutional neural network–derived AI-VA is a powerful indicator of vascular health and cardiovascular disease risk in a broad community-based sample.