Here, l is the long axis size of the particle, r a is the aspect ratio and K, a and b are fitting parameters. The shape factor of second-phase particles is denoted by α in equation 3. The equation proposed by Zener, despite being widely used in practical applications to predict the limiting mean grain size, is considered simplistic and underestimates the pinning effect of the particle distribution. Furthermore, the analytical models discussed earlier have a significant limitation in that they require consideration of the characteristic dimple shape of a grain boundary near a grain boundary-particle interface, which poses a challenge in achieving a better understanding of the pinning effect. The complexity of estimating the pinning force exerted by a distribution of particles, given that only a fraction of the particles will be located at grain boundaries, also necessitates the use of simplifying assumptions. This study aims to investigate the combined effects of multiple parameters on Zener pinning and improve its control by utilizing Phase-field modeling and machine-learning techniques. While analytical models have limitations in comprehending the pinning effect, Phase-field models have been used to analyze the impact of second-phase particle size, shape, and volume fraction on Zener pinning drag . However, a systematic investigation of the combined effects of these parameters has not been performed. As multiple parameters are often varied simultaneously in practical applications, a comprehensive examination of their combined effects is essential to improve Zener pinning control. To address this, Phase-field modeling is used to develop an ML model that determines the relative importance of various parameters, and a workflow (depicted in Figure ) combining high-throughput Phase-field simulations and machine learning is introduced to explore the 5-dimensional parameter space and gain insights into Zener pinning. Our approach developed in this study is of general-purpose and can be applied to a wide range of materials phenomena influenced by multiple variables. Our workflow provides a comprehensive understanding of the combined effects of individual behaviors within diverse systems. This versatile methodology enables us to precisely tune individual parameters, allowing us to exert inverse control over the phenomenon. This capability can be leveraged to optimize and refine control strategies for enhanced outcomes.