Hypertension is a silent killer, with over half of affected adults unaware of their condition^1,2. This lack of awareness is a major concern, as early intervention is critical for preventing major adverse cardiovascular events^3,4. Here we show that applying artificial intelligence (AI) to multimodal waveforms (photoplethysmography and accelerometry) recorded using a widely available consumer smartwatch (AI-PPG-ACC-HTN), without any cuff calibration, can detect hypertension with accuracy levels comparable to traditional cuffed blood pressure (BP) devices in the existing clinical framework. We validated AI-PPG-ACC-HTN in a prospective, multicenter study of 196 diverse participants free from known cardiovascular disease and antihypertensive medication against gold-standard 24-hour ambulatory BP monitoring. Over seven days of real-world monitoring, AI-PPG-ACC-HTN detected hypertension with a sensitivity of 65.8% (95% CI, 54.0%-76.3%), specificity of 90.0% (95% CI, 83.2%-94.7%), and positive predictive value (PPV) of 80.6% (95% CI, 68.6%-89.6%). In comparison, initial office BP screening achieved a sensitivity of 55.3% (95% CI, 43.4%-66.7%), specificity of 90.0% (95% CI, 83.2%-94.7%) and PPV of 77.8% (95% CI, 64.4%-88.0%). These results highlight an opportunity for consumer smartwatches to facilitate population-level opportunistic hypertension screening by prompting at-risk individuals toward clinical assessment and confirmatory diagnosis using established cuff-based methodologies, offering an accessible tool to address this major public health challenge.