Chronic-rhinosinusitis (CRS) is one of the most common conditions affecting ~14.2% (29.2-million) of US adults leading to estimated 18-22 million-physician office visits. It causes significant physical symptoms, negatively affects the quality-of-life and can substantially impair daily functioning. Various factors including microorganisms, allergies, and other inflammatory triggers play role in CRS. Lack of a universal marker and acknowledged difficulty in establishing the causes for the condition contributes to the poor treatment strategies and outcomes associated with CRS. Utilizing panel of sensitive markers associated with inflammatory responses in the nasal area can provide clinicians valuable information about the disease at the molecular level. The present study aims at identifying spectrochemical markers associated with the onset of CRS using data-driven Raman imaging. By combining high-resolution Raman imaging and machine learning we have developed a novel approach to obtain an integrated insight. Our findings are suggestive of differential changes in the biochemical composition of nasal tissues with CRS onset. A regression-based framework has been developed to link the inflammation score with spectral features. Support vector machine has been employed to explore the feasibility of classification. Successful recognition of these markers in nasal tissues will be helpful not only in designing automated diagnosis platforms but can also be used for identifying novel treatment strategies. Findings of this study will also serve as the foundation of our future research work on evaluating the applicability of nasal lavage for a minimally invasive method for objective CRS diagnosis.