Early Vascular Aging (EVA) is gaining prominence in preventive cardiovascular care; however, its large-scale adoption remains constrained by the lack of affordable and fully automated vascular assessment tools. While ultrasound provides a practical window into vascular health, most automated vessel analysis methods are designed for B-mode imaging and rely on specialized hardware and expert operation, limiting their deployment in community and resource-constrained settings. A-mode ultrasound offers a favorable frugal and edge-deployable alternative, but its single scan-line nature and reduced spatial redundancy pose significant challenges for robust artery detection. We propose a hybrid deep learning and signal processing framework for automated arterial wall detection from continuous A-mode acquisitions. The method stacks finite RF frames to generate motion-mode representations, recasting the problem as a pattern-recognition task. A lightweight YOLO-11n model was used to demonstrate feasibility. Trained on a human dataset of 82,585 images (57% positive), the model achieved 83.8% precision, 93% recall, and 92.9% mAP@0.5. Extensive in-silico validation using a purpose-built simulation testbed—spanning diverse noise and motion conditions. The method showed accuracy exceeding 97% for SNR > 10 dB. Accuracy remained above 90% across heart rates of 30–120 bpm, with the lower values observed at the lower heart rates. Performance was similarly consistent and high across all waveform types. Precision, recall and accuracy values were in similar ranges, indicating the methods ability to discern both artery’s presence or absence. These results establish the practical viability of the proposed hybrid A-mode pipeline for robust artery detection.