BACKGROUND: Stroke is a major complication of atrial fibrillation (AF), and risk prediction using the congestive heart failure, hypertension, age, diabetes, stroke, vascular disease, and sex category score (CHA₂DS₂-VASc) remains limited by residual heterogeneity. We aimed to identify plasma proteins associated with post-AF stroke and evaluate whether a protein score provides incremental predictive information beyond CHA₂DS₂-VASc. METHODS: We analyzed 709 AF participants from the UK Biobank Pharma Proteomics Project, with 76 incident strokes. Stroke-related proteins were identified using multivariable Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, and machine-learning approaches. A five-protein score was constructed, and its incremental value beyond CHA₂DS₂-VASc was assessed by discrimination, calibration, reclassification, clinical net benefit, and 1000-bootstrap internal validation. Mendelian randomization served as supportive genetic evidence. RESULTS: Five core proteins were identified: epidermal growth factor receptor (EGFR), V-type proton ATPase subunit D (ATP6V1D), neurotrophin 4 (NTF4), amnionless (AMN), and discoidin, CUB and LCCL domain-containing protein 2 (DCBLD2). Adding the five-protein score to CHA₂DS₂-VASc improved discrimination, increasing the concordance index from 0.681 to 0.768. The combined model had 3-, 5-, and 8-year receiver operating characteristic areas of 0.799, 0.775, and 0.806, respectively, and showed favorable five-year prediction error and calibration, with a Brier score of 0.0347, calibration intercept of -0.068, and calibration slope of 0.968. The score improved continuous net reclassification improvement (0.459; P = 0.028). Mendelian randomization provided supportive genetic evidence for AMN, EGFR, and DCBLD2. CONCLUSIONS: The five-protein score provided incremental predictive information beyond CHA₂DS₂-VASc for post-AF stroke risk assessment.