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Stem cell‐based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non‐destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping‐based machine learning analysis is reported to distinguish differentiating human adipose‐derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single‐cell resolution. The label‐free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton‐based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix‐assisted laser desorption/ionization‐mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label‐free identification of stem cell differentiation with high spatial and chemical resolution.