Background <p>Lupus nephritis (LN) represents a serious renal manifestation of systemic lupus erythematosus and is driven by intricate interactions among immune dysregulation, inflammatory responses, and metabolic alterations. Recent studies have found that the metabolic dysregulation of taurine contributes to the progression of renal injury by impairing its antioxidant and immunomodulatory functions.</p> Methods <p>This study utilized microarray data of LN from the Gene Expression Omnibus (GEO) database and applied three Machine Learning (ML) algorithms—Least Absolute Shrinkage and Selection Operator, Support Vector Machine, and Boruta—to screen key genes and construct a diagnostic model. The model was validated both internally and externally. Single-Sample Gene Set Enrichment Analysis (ssGSEA) and the CIBERSORT algorithm were employed to assess the association between the model genes and immune infiltration. Additionally, potential biological mechanisms were explored through functional enrichment analysis, disease subtype classification, and prediction of the transcription factor regulatory network.</p> Results <p>This study identified two taurine metabolism-associated diagnostic genes (CCND1 and LYN) for LN via differential expression analysis and ML. The validated diagnostic model demonstrated high accuracy, while further analyses revealed their involvement in pro-inflammatory immune infiltration (e.g., increased monocytes/neutrophils) and immune-related pathways (e.g., NOD-like receptor signaling). LN patients were sub-classified into two immune-distinct subtypes, and upstream regulators (e.g., STAT3, TP53) of CCND1 were predicted.</p> Conclusion <p>This study identified CCND1 and LYN as promising diagnostic biomarkers for lupus nephritis, linking them to dysregulated taurine metabolism and immune infiltration, thereby offering new insights into the disease’s immunometabolic interplay and potential therapeutic avenues.</p>