Sinusitis is a common inflammatory condition of the paranasal sinuses that lacks a rapid and objective diagnostic tool. In this study, we investigated the potential of Raman spectroscopy of serum combined with machine learning for the noninvasive diagnosis and classification of sinus inflammation. Raman spectra were collected from serum of patients with clinically diagnosed sinusitis and from healthy controls. Although chemometric analysis, including principal component analysis (PCA), revealed only subtle spectral intensity differences between studied groups, machine learning provided clear discrimination between patients from control group and group of patients with sinusitis. Feature importance analysis identified several Raman shifts related to amide I and CH stretching regions (1142–1153 cm−1, 1469–1473 cm−1, 1495–1511 cm−1, 1663–1746 cm−1 and 2840–3000 cm−1) as the most relevant for distinguishing sinusitis from control samples. In patients with varying degrees of disease advancement, additional discriminative wavenumbers (801–954 cm−1 and 1611–1690 cm−1) were found, suggesting that inflammation severity may be reflected in protein-related vibrations. Decision tree models confirmed the diagnostic significance of these regions, particularly around 1616–1700 cm−1. Machine learning models, including: Random Forest, Decision Tree, Support Vector Machine (SVM), and LightGBM were trained and validated using the Leave-One-Group-Out (LOGO) cross-validation strategy, where data partitioning was performed at the patient level. The models achieved excellent classification performance in differentiating sinusitis patients from healthy controls, with SVM (grid search optimized) reaching perfect results (balanced accuracy = 1.000 ± 0.000). Feature selection did not reduce performance, confirming that the most relevant spectral regions contain sufficient diagnostic information. Classification between patients with inflammatory changes in all sinuses affected and those with only partial involvement also yielded satisfactory accuracy, particularly for SVM and Random Forest models. Obtained results demonstrate that Raman spectroscopy of serum, combined with machine learning, enables highly accurate differentiation between healthy and sinusitis-affected individuals and may serve as a promising noninvasive approach for the biochemical assessment and monitoring of sinus inflammation.