Accurate assessment of the surface morphology of face gears after machining is essential for optimizing their operational efficiency and extending their service life. However, several practical challenges complicate this process, including variations in abrasive characteristics, grinding conditions, and mechanical vibrations. Developing robust models to characterize these surface features is a complex task that involves addressing issues related to computational precision and material behavior during the grinding process. This study introduces a dynamic contour-based sampling approach to enhance the accuracy of surface topography estimation. By integrating the principles of elastic contact mechanics with a comprehensive simulation of grain interaction during successive grinding passes, this approach captures the dynamic variations in the workpiece surface throughout the process. Furthermore, the methodology incorporates the influence of localized deformation within the contact area and accounts for the interaction between consecutive grinding passes, which contributes to the refinement of the surface prediction model. Experimental validation confirms that the model provides highly accurate representations of the surface topography, aligning closely with measured data.