This study developed a machine learning ensemble model to efficiently screen for potential errors in clinical chemistry testing and reduce unnecessary retests. The model’s effectiveness was validated using multicenter data, with clinical records from Asan Medical Center utilized for development and internal validation. Features such as modified z-scores, scaled test results, and newly defined error flags were adopted as input variables. Internal validation yielded an accuracy of 0.925 and a 73.05% reduction in unnecessary retests. Subsequent independent external validations at Haeundae and Sanggye Paik Hospitals resulted in accuracy of 0.768, with retest reductions of 67.08% and 22.34%, respectively. These results demonstrate strong generalization performance across diverse multicenter environments, improving laboratory workflow efficiency by proactively reducing retest rates.