<p>In diffusion bonding of dissimilar alloys, the closure of interfacial voids and the diffusion of elements across the interface are critical factors in achieving a high-quality joint. In the present study, a comprehensive model was developed to simulate the diffusion bonding of Stainless Steel 304 (SS-304) and Inconel-718 (IN-718). The model integrates a finite element analysis (FEA)-based deformation mechanism with a numerical diffusion framework to capture both mechanical and atomic-level phenomena occurring at the interface. The primary objective was to evaluate the influence of bonding pressure and temperature on the bonded ratio of the SS-304/IN-718 joint. Results indicated that increasing either the bonding pressure or the temperature significantly reduced the time required to achieve a specified bonded ratio. This relationship between bonded time and bonded ratio was found to follow an exponential trend. In order to validate the model, the simulated void sizes were compared with experimental observations, and exhibited an error of less than 0.1&#xa0;μm, thus confirming the accuracy of the void closure prediction. Additionally, the elemental diffusion model was used to determine the spatial and temporal concentration profiles across the interface, yielding weight percent values of diffused elements. The diffusion predictions were experimentally validated, with an average error of approximately 15%. Furthermore, in order to predict the bonded ratio across a wide range of pressure and temperature conditions, machine learning models were employed. Among the models tested, the Random Forest and Gradient Boosting algorithms demonstrated the highest accuracy, achieving a mean square error of approximately 0.0005. This integrated modeling approach offered a robust framework for understanding and optimizing diffusion bonding in dissimilar alloys systems.</p>