This paper presents a method for evaluating ischemic stroke risk through the quantification of carotid artery stenosis using deep learning techniques. By employing U-Net architecture, we achieve accurate segmentation of carotid arteries from longitudinal and transverse 2D ultrasound images. This allows for precise measurement of artery diameter and lumen area reduction. Furthermore, we produce a comprehensive 3D model of carotid artery, enhancing the assessment of stenosis severity, a major contributing factor to ischemic strokes, which constituted 65.3% of all new strokes globally in 2025. Our AI-powered approach enhances consistency and accuracy, offering significant improvements over traditional manual methods, potentially aiding in timely intervention and stroke prevention.