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IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4+T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4+T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10–0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time. • IgG4-RD patients had altered immunological subsets, by which can be distinguished from healthy people. • Machine-learning approach was effective to detect the features of immunophenotype of IgG4-RD and to identify the heterogeneity of IgG4-RD. • IgG4-RD patients with elevated of CD4+ memory T cells were at higher risk for relapse, calling for more attention during follow-up. [ABSTRACT FROM AUTHOR]