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Objective This study aimed to leverage machine learning algorithms to explore the relationship between anti-double-stranded DNA (anti-dsDNA) immunoglobulin G (IgG) glycosylation and the degree of organ involvement in patients with SLE.Methods and analysis We enrolled 86 consecutive treatment-naïve patients with SLE positive for anti-dsDNA antibodies from the Department of Rheumatology and Immunology at Ruijin Hospital, Shanghai, between 2017 and 2019. We quantified and classified the degree of organ involvement in patients with SLE and analysed each glycoform and a combination of glycoforms of purified anti-dsDNA IgG. A random forest classifier and artificial neural network were applied to evaluate the correlation between the levels of glycoform pairs and the degree of organ involvement.Results Pearson’s correlation analysis revealed a strong connection between the involved and uninvolved organs in patients with SLE. Random forest analysis showed that the combination of IgG1Gal and IgG3/4Bis had the highest accuracy (0.7692) and area under the curve (0.8187). In terms of predicting the degree of involvement using an artificial neural network, IgG3/4Bis and IgG1Gal showed the lowest mean squared error (0.0244).Conclusions Our study showed the effectiveness of combining glycoforms to classify and predict the degree of SLE organ involvement. Different glycoforms were correlated with the involvement degree to various extents, and the combination of anti-dsDNA IgG3/4Bis and IgG1Gal exhibited the best correlation with organ involvement.