Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus that significantly impacts renal prognosis. Accurate diagnosis, classification, and timely therapeutic interventions remain crucial for preventing chronic kidney disease progression and end-stage renal disease. This paper presents a modular expert system designed to support clinicians in the comprehensive management of LN, from diagnosis through treatment planning to post-therapy evaluation. The system integrates machine learning algorithms—including neural networks and ensemble methods—with rule-based logic, providing transparent, interpretable decision support. Multiple functional modules were developed, including diagnostic support, biopsy-based severity classification using a MIMO (Multiple-Input Multiple-Output) architecture, treatment recommendation aligned with KDIGO (Kidney Disease: Improving Global Outcomes) guidelines, and automated post-treatment response assessment. A comparative analysis with existing nephrology AI (Artificial Intelligence) systems confirms the proposed system’s superior modularity, interpretability, and clinical applicability. The solution demonstrates how explainable, AI-driven expert systems can enhance nephrology practice and facilitate personalized, data-driven care in LN management.