Plasmonic nanostructure-enabled label-free surface-enhanced Raman spectroscopy (SERS) emerges as a rapid nondestructive molecular fingerprint characterization technique for complex biological samples. However, label-free SERS bioanalysis faces challenges in reliability and reproducibility due to SERS signals' high susceptibility to local optical field variations at plasmonic hotspots, which can bias correlations between the measured spectroscopic features and the actual molecular concentration profiles of complex biochemical matrices. Herein, we report that plasmonically enhanced electronic Raman scattering (ERS) signals from metal nanostructures can serve as a SERS calibration internal standard to improve multivariate analysis of living biological systems. Through side-by-side comparisons with noncalibrated SERS datasets, we demonstrate that the ERS-based SERS calibration can enhance supervised learning classification of label-free living cell SERS spectra in (1) subtyping breast cancer cells with different degrees of malignancy and (2) assessing cancer cells' drug responses at different dosages. Notably, the ERS-based SERS calibration has the advantages of excellent photostability under laser excitation, no spectral interference with biomolecule Raman signatures, and no occupation competition with biomolecules at hotspots. Therefore, we envision that the ERS-based SERS calibration can significantly boost the multivariate analysis performance in label-free SERS measurements of living biological systems and other complex biochemical matrices.