Coronary heart disease (CHD) and carotid artery disease (CAD) often co-occur. However, conventional diagnosis typically involves separate, site-by-site examinations after symptoms appear, leading to delayed intervention. In this work, we developed a wearable ultrasound system that enables synchronous monitoring of cardiac and carotid dynamics for comorbidity assessment. The system combines dual wearable ultrasound patches, a synchronous imaging strategy, artificial intelligence-based image processing algorithms, and human circuitry models to automatically extract and analyze key cardiac-carotid metrics, such as heart rate, pulse rate, cardiac volume, cardiac output, and carotid blood pressure. By evaluating the correlation of these metrics between modeling and measurements, we showed the feasibility of differentiating among healthy participants and patients with CAD, CHD, or CAD-CHD comorbidity. This integrated approach constitutes a promising framework for supporting the proactive assessment of coronary-carotid comorbidity.