Abstract Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common monogenic form of vascular cognitive impairment and dementia. A genetic arteriolosclerotic disease, the molecular mechanisms driving vascular brain degeneration and decline remain unclear. With the goal of driving discovery of disease-relevant biological perturbations in CADASIL, we used machine learning approaches to extract proteomic disease signatures from large-scale proteomics generated from plasma collected from three distinct cohorts in US and Colombia: CADASIL-Early ( N = 53), CADASIL-Late ( N = 45), and CADASIL-Colombia ( N = 71). We extracted molecular signatures with high predictive value for early and late-stage CADASIL and performed robust cross- and external-validation. We examined the biological and clinical relevance of our findings through pathway enrichment analysis and testing of associations with clinical outcomes. Our study represents a model for unbiased discovery of molecular signatures and disease biomarkers, combining non-invasive plasma proteomics with clinical data. We report on novel disease-associated molecular signatures for CADASIL, derived from the accessible plasma proteome, with relevance to vascular cognitive impairment and dementia.