Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), pose a significant global health challenge, currently affecting over 40 million individuals and placing a heavy burden on healthcare systems. Existing diagnostic methods, such as neuroimaging and cognitive assessments, often lack sufficient sensitivity and specificity, especially in the early stages of disease. This underscores the need for novel biomarkers. Extracellular vesicles (EVs), particularly those derived from the brain, i.e., brain-derived extracellular vesicles (BDEVs), hold great potential as noninvasive diagnostic tools due to their ability to reflect the physiological and pathological states of their cells of origin. However, isolation and detection of such EV subpopulations from accessible body fluids such as blood remain a technical challenge due to their low abundance and overlapping physical properties compared to other EVs. This review discusses rationally designed isolation and detection technologies for EVs from major brain cell subpopulations and their integration with emerging fields like AI and big data analysis. We specifically contrast traditional ensemble-averaged bulk measurements with emerging single-entity assays, highlighting how the latter bypass biological noise to resolve rare BDEV subpopulations. It highlights the potential of these EV subpopulations as biomarkers, addresses EV isolation challenges and proposes standardized methodologies, and emphasizes the need for comprehensive profiling of EV markers at single-entity level, and point-of-care testing (POCT) development.