<p>Hyperlipidemia is characterized by elevated levels of lipids including cholesterol and triglycerides in the bloodstream that significantly increases the risk of many cardiovascular diseases. The aim of this study is to detect and screen elevated levels of cholesterol and triglycerides in hyperlipidemia patients by using surface-enhanced Raman spectroscopy (SERS). This technique provides molecular-specific information allowing for the identification of subtle spectral differences that are indicative of pathological conditions. SERS offers a rapid, sensitive, and non-invasive alternative for the detection of lipids by enhancing the Raman scattering signals, enabling the identification and quantification of these molecules and their biochemical components at very low concentrations. The screening and diagnostic ability of SERS was further confirmed by using multivariate statistical tools including partial least square regression analysis (PLSR) and principal component analysis (PCA). PCA is found very helpful for the classification of the SERS spectral groups of hyperlipidemia (hypercholesterolemia and hypertriglyceridemia) and healthy samples. PLSR is used for the quantification of the levels of cholesterol and triglycerides based on differentiating SERS spectral features of the hypertriglyceridemia and hypercholesterolemia patients and healthy serum samples. In this regard, the PLSR model was found very useful as indicated by the root mean squared error of prediction (<i>R</i>²) value of 0.912 for hypertriglyceridemia serum samples and 0.934 for hypercholesterolemia serum samples. This may prove to be quite useful in the future in diagnostic labs for the detection of cholesterol and triglycerides in hyperlipidemia, hypertriglyceridemia, and hypercholesterolemia in this study with an already built PLSR model.</p>