In the diagnosis, disease monitoring, and prognosis evaluation of autoimmune diseases, laboratory markers such as autoantibodies hold significant value. However, traditional testing practices face challenges including suboptimal strategies, low positive predictive value, and wastage of medical resources. Evidence-based medicine emphasizes guiding clinical decisions based on the best available research evidence, while machine learning, with its powerful data mining and analytical capabilities, provides robust support for the management and selection of clinical laboratory tests. This article explores the value of a novel strategy for autoimmune disease testing project selection, grounded in evidence-based medicine and machine learning, in disease diagnosis, stratified management, and prognosis evaluation. Using systemic lupus erythematosus as an example, a novel test pannel strategy for autoimmune disease testing panels is constructed. This novel strategy demonstrates significant potential in enhancing diagnostic efficacy, strengthening dynamic management, and optimizing the allocation of medical resources. However, its clinical implementation still faces multiple challenges, including standardization of testing platforms, quality of training data, and integration with healthcare information systems. In the future, through the accumulation of multi-center data and deep integration with clinical decision support systems, it is expected to establish a novel strategy for autoimmune disease testing panel selection that optimizes quality, efficiency, and cost.