Engineering surfaces commonly exhibit multiscale topographical features that significantly influence contact, friction, and sealing behavior. Accurate characterization of surface topography across a wide range of spatial scales remains challenging due to the limited bandwidth and measurement uncertainties inherent to a single instrument system. This study meets this gap by introducing a Bayesian regression framework for integrating topographies obtained from multiple optical instruments. A wide-bandwidth power spectral density function is established from the fused data, and its associated uncertainty is quantitatively assessed using Gaussian process regression. The proposed method is validated through measurements on a finely ground sapphire wafer and comparison with scanning electron microscope images. The methodology provides a robust solution for surface characterization in contact mechanics and tribological analysis.