The resulting random forest regression model can predict grain sizes based on five Phase-field model parameters. The model's performance was evaluated using both training and test datasets, achieving high accuracy with an average prediction error of 0.72 nm and 1.44 nm, respectively. The SHapley Additive exPlanations (SHAP) approach was used to determine the relative importance of each parameter, providing critical insights into the underlying mechanisms of Zener pinning. The findings reveal that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. This understanding will enable us to select appropriate second-phase particles to optimize final grain size and design materials with desired mechanical properties.