Our workflow provides a surrogate ML model that addresses the computational expense of phase-field simulations, enabling a more efficient and effective approach to extract key features and explore the impact of various parameters on Zener pinning. The results of this study have significant implications for improving material properties and advancing materials engineering. This approach can be extended to other materials systems and will enable us to develop a fundamental understanding of the role of various parameters in Zener pinning. By gaining a better understanding of the underlying mechanisms of grain coarsening and Zener pinning, we can design materials with desired grain structures and mechanical properties.