Extracellular vesicles (EVs) have emerged as promising biomarkers in liquid biopsy due to their stability and ability to reflect the molecular characteristics of their cells of origin. However, upon release into the bloodstream, EVs acquire a corona of plasma proteins that can obscure their surface markers, limiting their diagnostic utility. The aim of this study was to demonstrate that protein corona (PC) removal significantly enhances the clinical diagnostic performance of EV-based assays. Plasma-derived EVs from healthy controls and patients with lung cancer were trypsinized to strip off the PC layer, generating peeled-off plasma EVs. Surface-enhanced Raman scattering was employed to probe the surface molecular features of peeled-off and unmodified EVs. A total of 10,000 Raman spectra were acquired and analyzed using various machine learning models. Removal of the PC layer resulted in consistent improvements in diagnostic classification accuracy: principal component analysis (64% to 76%), t-distributed stochastic neighbor embedding (68 to 84%), and convolutional neural network (90 to 95%), with an average performance gain of 11 ± 5.57%. These results indicate that eliminating the PC layer improves the resolution of biologically meaningful differences and enhances the accuracy of EV-based liquid biopsy for early cancer detection.