Raman spectroscopy (RS) has become an imperative instrument in biomedical research. It is a nondestructive and label-free laser spectroscopic technique, which can identify chemical composition of a biological sample by detecting multiple molecular components simultaneously without the need or minimal sample preparation under physiological condition. A major limitation while analyzing Raman spectra of a biological sample is the overlapping nature of vibrational modes of different molecules at a single spectral peak. Machine learning (ML) techniques encompass various multivariate and statistical analysis methods, which can be broadly classified into supervised and unsupervised techniques. Multivariate curve resolution-alternating least square (MCR-ALS) technique is a multivariate ML model that can potentially deconvolute overlapping Raman vibrational modes and extract the meaningful spectra that can be associated with biochemical components along with their concentrations. In this study, Raman spectral data were acquired from osteoarthritic chondrocytes. The spectra of three cellular components associated with DNA, proteins, and lipids were extracted using MCR-ALS technique, and it was observed that concentration of DNA and proteins decreased while concentration of lipids increased with progression of osteoarthritic grades. The study indicated that combined approach of Raman-MCR analysis can open new possibilities for improved Raman spectral analysis, which might assist further in providing the meaningful interpretation of complex Raman spectra originated from the biological samples.