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Stroke is the most common cause of morbidity and death in the Western world, following ischemic heart disease and cancer. Stroke can be of two types, ischemic or hemorrhagic, with ischemic stroke accounting for approximately 85% of the total number of strokes. Well-recognized environmental risk factors for stroke include hypertension, smoking, diabetes mellitus, atrial fibrillation, and atherosclerosis. Computed tomography (CT) scanning is used to diagnose hemorrhagic stroke but is relatively ineffective and may remain normal in patients with mild ischemic strokes. Magnetic Resonance Imaging (MRI) is more sensitive in detecting ischemia than CT, especially in the diagnosis of mild stroke but it is still not 100% sensitive or precise. A simple and low-cost, rapid blood test to confirm a clinical and imaging diagnosis of ischemic stroke would be extremely useful. Based on this, the central idea of this paper is to develop a method that would be applicable to a statistically viable sample set to provide candidate biomarkers for distinguishing stroke types. In search of these candidate biomarkers, different analytical separation techniques have been used to screen for major differences in the proteomes of patients plasma samples with proteomics for identification.