Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare disease that requires accurate diagnostic methods to overcome the limitations of current diagnostic criteria relying on clinical observation. Eosinophils from EGPA undergo dynamic changes that reflect its pathological state. Thus, physicochemical analysis of EGPA eosinophils may serve as a key diagnostic approach. In this study, eosinophils from patients with EGPA (n = 8) and healthy control group (n = 7) were analyzed using a multimodal imaging system with aligned optical diffraction tomography (ODT) and Raman spectroscopy. Given that ODT allows for physical analysis and Raman shift indicates chemical components, combining these two different analyses approaches could improve the accuracy of classification. We applied partial least squares discriminant analysis (PLS-DA) on Raman spectrum, specifically focusing on k-means clusters, corresponding to high refractive index (RI) regions identified by ODT. This process aimed to extract variable importance projection (VIP) score, to analyze key feature of Raman spectrum for chemical profiling. Chemical profiling from Raman spectrum complementarily explains the high RI value in granules. These granules enriched areas, derived from ODT physical feature with the validation of Dice-similarity Coefficient, enhanced the accuracy of EGPA prediction up to 96%. This integrated approach demonstrates the potential of multimodal profiling to enhance diagnostic precision for eosinophilic disorders.