This study explores the development of a modular expert system designed to automate key aspects of nephrological care, focusing on prediction, classification, and decision support. The research covers initial results from two implemented modules—predicting laboratory test results using machine learning and supporting risk classification in idiopathic membranous nephropathy. Machine learning models such as the adaptive input-output predictive model demonstrated high performance in predicting laboratory results with high accuracy. The risk classification system for idiopathic membranous nephropathy used rule-based expert algorithms and machine learning to improve clinical decision making, achieving decision accuracy superior to historical clinical decisions. These preliminary findings highlight the potential of AI to enhance diagnostic accuracy, streamline patient care, and improve outcomes in nephrology.