With their important role in regulating intracellular redox balance and maintaining cell homeostasis, endogenous mercaptans are recognized as biomarkers of many diseases in clinical practice, and thus establishing efficient yet simple methods to distinguish and quantify endogenous mercaptans is of great significance for health management. Here, we propose a machine learning-enabled time-resolved nanozyme-encoded strategy to identify endogenous mercaptans in the presence of potential interferents for disease diagnosis. Diethylenetriaminepenta(methylenephosphonic) acid was first employed to coordinate with Mn