In mountainous landscapes, integrating topographical information is crucial for effective analysis and understanding. Remote sensing becomes indispensable in studying mountains, enabling the monitoring of critical aspects such as grassland degradation, snow depth, glacier dynamics. The intricate nature of mountain ecosystems requires a thorough understanding of their dynamics, given their vital role in providing habitats for unique species, influencing hydrology patterns, and indicating the impact of climate change. However, the persistent challenge of cloud cover obstructs surface observations, limiting the effectiveness of optical sensors. To overcome this obstacle, various cloud removal techniques have been developed, although they tend to struggle in such landscapes. In this study, we introduce CRT-UNet, a UNet-based cloud removal model that incorporates topographical information from digital elevation model (DEM) data as input to enhance performance in mountainous regions. By integrating synthetic aperture radar Sentinel-1 (S1) data, DEM information, and topographic insights into Sentinel-2 (S2) data, our model aims to enhance cloud removal capabilities, particularly in challenging terrains characterized by thick cloud coverage and significant elevation variations. This integration of topographical information enriches the cloud removal process, enabling more accurate restoration of obscured terrain features. The results demonstrate the superior performance of CRT-UNet in cloud removal across varying cloud cover levels and complex terrain features, such as rugged peaks and deep valleys. The proposed model outperforms state-of-the-art cloud removal models quantitatively and qualitatively. This underscores the importance of incorporating topographical information in remote sensing applications, particularly in mountainous regions, to improve data accuracy.