Individuals who have kidney disease or kidney transplants need routine assessment of their kidney damage and function, which are largely measured based on histological examination of kidney biopsies, blood test, and urinalysis. These methods are practically difficult or inconvenient, and expensive. The objective of this study was to develop a model to estimate the kidney damage and function by surface-enhanced Raman spectroscopy (SERS). Urine samples were collected from two previous studies: renal allograft recipient Lewis rats receiving anti-TGF-β antibody or control antibody treatment and obese diabetic ZSF1 rats with kidney disease fed with whole grape powder-containing chow or control chow. Silver nanoparticle-based SERS spectra of urine were measured. SERS spectra were analyzed using principal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squires (PLS) analysis. PCA/LDA separated anti-TGF-β antibody-treated group from control group with 90% sensitivity and 70% specificity in kidney transplants, and grape-fed group from controls with 72.7% sensitivity and 60% specificity in diabetic kidneys. The receiver operating characteristic curves showed that the integration area under the curve was 0.850 ± 0.095 (p = 0.008) in kidney transplant groups and 0.800 ± 0.097 (p = 0.02) in diabetic kidney groups. PLS predicted the biochemical parameters of kidney function using the SERS spectra, resulting in R2 = 0.8246 (p < 0.001,urine protein), R2 = 0.8438 (p < 0.001, urine creatinine), R2 = 0.9265 (p < 0.001, urea), R2 = 0.8719 (p < 0.001, serum creatinine), and R2 = 0.6014 (p < 0.001, urine protein to creatinine ratio). Urine SERS spectral analysis suggesting that it may become a convenient method for rapid assessment of renal impairment.