Vestibular migraine (VM) lacks reliable biomarkers, leading to delayed diagnosis and unnecessary testing. This retrospective cohort study evaluated whether computerized dynamic posturography (CDP) improves diagnostic accuracy beyond clinical features alone. Among 334 patients (46 VM), machine learning models using CDP plus clinical features achieved sensitivity of 82.8% and AUC of 0.86, outperforming CDP-only models (AUC ~0.53) and modestly improving upon clinical-only models (AUC ~0.80). SHAP analysis revealed that clinical symptoms—photophobia, phonophobia, episode duration—were the strongest predictors, while CDP contributed incremental value (+0.06 AUC), particularly in ruling out VM with high negative predictive value (~96%). These findings support the use of combined clinical and biomechanical data for VM risk stratification and demonstrate potential for CDP-assisted triage to reduce unnecessary vestibular workups. Prospective multicenter validation is warranted.