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Here, this study introduces an integrated platform that combines engineered cell\u2010derived nanovesicles, used as model standards with controlled surface protein states, and machine learning\u2010optimized impedance spectroscopy. Cell\u2010derived nanovesicles with defined surface protein densities are generated by extruding HeLa cells expressing 1, 3, or 9 copies of amyloid\u2010\u03b2 42, establishing a series of reference vesicles with precisely controlled oligomeric configurations. Through systematic evaluation of impedance features across frequencies from 10\u00a0Hz to 1\u00a0MHz, machine learning identifies reactance changes at 1\u00a0kHz as optimal for distinguishing oligomeric states on the vesicle membrane. Equivalent\u2010circuit modeling reveals that membrane capacitance correlates with protein oligomerization, and structural predictions explain the mechanistic basis. The platform also enables label\u2010free, time\u2010resolved analysis of A\u03b2 oligomer formation directly on vesicular membranes, providing insights into aggregation dynamics. This platform establishes standardizable reference materials using engineered nanovesicles and a quantitative framework for extracellular vesicle surface protein analysis, offering a broadly applicable method for studying membrane\u2010associated protein dynamics relevant to neurodegenerative diseases and advancing extracellular vesicle\u2010based diagnostics.<\/jats:p>","DOI":"10.1002\/advs.77452","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T08:05:46Z","timestamp":1787990746000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine\u2010Learning\u2010Assisted Impedance Component Analysis Enables Standardizable Surface Protein Analysis of Extracellular Vesicles Using Engineered 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