Objective To explore the potential mechanism of the occurrence and development of lupus nephritis (LN) and identify key biomarkers and immune-related pathways associated with the progression of LN. Methods We downloaded a dataset from the Gene Expression Omnibus database. By analyzing the differential expression of genes and performing weighted gene co-expression network analysis (WGCNA), as well as Gene Ontology enrichment, Disease Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, we explored the biological functions of differentially expressed genes in LN. Using three machine learning models, namely LASSO regression, support vector machine, and random forest, we identified the hub genes in LN, and constructed a line diagram diagnosis model based on the hub genes. The diagnostic accuracies of the hub genes were evaluated using the receiver operating characteristic curve, and the relationship between known marker gene sets and hub gene expression was analyzed using single sample gene set enrichment analysis. Results We identified a total of 2297 differentially expressed genes. WGCNA generated 7 co-expression modules, among which the cyan module had the highest correlation with LN. We obtained 347 target genes by combining differential genes. Using the three machine learning methods, LASSO regression, support vector machine, and random forest, we identified three hub genes (CLC, ADGRE4P, and CISD2) that could serve as potential biomarkers for LN. The area under the receiver operating characteristic curve (AUC) analysis showed that these three hub genes had significant diagnostic value (AUCCLC=0.718, AUCADGRE4P=0.813, AUCCISD2=0.718). According to single sample gene set enrichment analysis, the hub genes were mainly associated with apoptosis, glycolysis, metabolism, hypoxia, and tumor necrosis factor-α-nuclear factor-κB-related pathways. Conclusions By combining WGCNA and machine learning techniques, three hub genes (CLC, ADGRE4P, and CISD2) that may be involved in the occurrence and development of LN are identified. These genes have the potential to aid in the early clinical diagnosis of LN and provide insight into the mechanisms underlying LN progression.