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Cell therapy using proliferating human hepatocytes (ProliHHs) is an effective treatment approach for advanced liver diseases. However, rapid and accurate identification of high -quality ProliHHs from different donors is challenging due to individual heterogeneity. Here, we developed a machine learning framework to integrate single -cell Raman spectroscopy from multiple donors and identify different stages of ProliHHs. A repository of more than 14,000 Raman spectra, consisting of primary human hepatocytes (PHHs) and different passages of ProliHHs from six donors, was generated. Using a sliding window algorithm, potential biomarkers distinguishing the different cell stages were identified through differential analysis. Leveraging machine learning models, accurate classification of cell stages was achieved in both within -donor and cross -donor prediction tasks. Furthermore, the study assessed the relationship between donor and cell numbers and its impact on prediction accuracy, facilitating improved quality control design. A similar workflow can also be extended to encompass other cell types.