To predict acute ischemic stroke (AIS) in large-artery atherosclerosis (LAA), we combined serum proteomics with machine learning to identify diagnostic biomarkers. Using a dual-cohort design, we analyzed proteins and validated targets through advanced proteomic techniques. Machine learning identified two protein panels: one distinguishing AIS from LAA and another differentiating AIS/LAA from healthy controls. These panels capture dynamic pathophysiological processes, offering scalable tools for stroke prediction in high-risk LAA populations, surpassing conventional anatomical assessments.