White matter lesions (WMLs) have an impact on neuronal connectivity; and consequently affect balance, mobility and cognition in both normal aging and disease states. Using a fully automated segmentation algorithm and multi-modal images, we estimated WMLs volumes to predict the clinical severity in a cohort of Parkinson’s disease (PD) patients and healthy controls (HC). Increased WMLs volume is strongly associated with both motor/gait and cognitive dysfunctions in PD. Lobar WMLs are found to have differential impact on distinctive cognitive domains. Automated volumetric quantification of WMLs load, particularly within the frontal and prefrontal regions can predict severity of symptoms in PD.