<p>This study presents an innovative stochastic simulation model designed for analyzing and predicting microstructural evolution in contemporary manufacturing processes. The framework integrates multiscale modeling techniques with probabilistic approaches to account for inherent variability in material behavior and manufacturing parameters. By incorporating Monte Carlo methods with cellular automata, the model effectively captures both spatial and temporal aspects of microstructural changes. The methodology enables accurate predictions of grain growth, phase transitions, and defect formation based on precise process condition data. Case studies utilizing experimental data from selective laser melting and directed energy deposition demonstrate the effectiveness of the framework in estimating the final microstructure of manufactured components. The analysis results indicate a 92% correlation between the predicted and actual grain size distributions and demonstrate the framework’s capability to forecast texture evolution and phase precipitation patterns. This advanced computational approach provides manufacturers with crucial tools for continuous process optimization, thereby enhancing quality control and improving the reliability and functionality of high-performance manufactured products.</p>