Abstract: Corrosion rates of biodegradable Zn alloys are directly related to their post-implantation safety and effectiveness. Currently, no highly accurate and interpretable “white-box” machine learning models exist for corrosion rate predictions. This study proposes a data-driven method coupled with accelerated corrosion testing for predicting the corrosion rates of biodegradable Zn–0.45Mn–0.2Mg (wt%) alloy. A symbolic regression (SR) machine-learning model was established for the first time based on an analytical expression of the corrosion rate and four corrosion parameters. Outperforming five other machine learning models, the SR model achieved a determination coefficient of 0.97 and prediction errors in the verification experiments of less than 10%. This study marks a paradigm shift from qualitative to quantitative analysis for corrosion research on biodegradable metals.