This study evaluates the role of quantitative characteristics of white matter hyperintensities (WMHs) in predicting the 1-year recurrence risk of ischemic stroke. We conducted a retrospective analysis of 1061 patients with ischemic stroke from January 2018 to April 2021. WMHs were automatically segmented using a cluster-based method to quantify their volume and number of clusters (NoC). Additionally, two radiologists independently rated periventricular and deep WMHs using the Fazekas scale. The cohort was divided into a training set (70%) and a testing set (30%). We employed Cox proportional hazards models to develop predictors based on quantitative WMH characteristics, Fazekas scores, and clinical factors, and compared their performance using the concordance index (C-index). A total of 180 quantitative variables related to WMHs were extracted. A higher NoC in deep white matter and brainstem, advanced age (>90years old), specific stroke subtypes, and absence of discharge antiplatelets showed stronger associations with the risk of ischemic stroke recurrence within 1year. The nomogram incorporating quantitative WMHs data showed superior discrimination compared to those based on the Fazekas scale or clinical factors alone, with C-index values of 0.709 versus 0.647 and 0.648, respectively, in the testing set. Notably, a combined model including both WMHs and clinical factors achieved the highest predictive accuracy, with a C-index of 0.735 in the testing set. Quantitative assessment of WMHs provides a valuable neuro-imaging tool for enhancing the prediction of ischemic stroke recurrence risk.