Glacier visualization is critical for studying climate change as glaciers respond rapidly to global warming. It serves as an indicator of environmental changes and provides insights into the magnitude of climate change. Traditional on-site photography faces challenges such as safety hazards, high costs, and tight time constraints. Existing terrain generation models have limitations such as not being capable of using real satellite imagery to control generated terrain and achieving photorealistic image quality. To address these issues, this study introduces GlacierPix2Pix, a novel deep learning model that generates glacier scenery from satellite images. This study uses the Landscape HQ dataset containing 90K terrain images and the USGS Glacier Benchmark dataset containing Digital Elevation Models of 5 different glaciers over many years. This study curates a dataset of over 9K glacier images that can be used in related studies from the LHQ dataset. Glacier contours are extracted from the DEM images and glacier images. Based on the StyleGAN architecture, GlacierPix2Pix comprises a terrain generator, discriminator, and style encoder, and it supports both 2D and 3D methods. The model achieves a Frchet Inception Distance (FID) of 31.90, which surpasses the performance of other similar datadriven methods. It can successfully produce photorealistic glacier images aligned with satellite-derived contours. This research contributes to a better understanding of the urgent issues surrounding climate change, especially its effect on glaciers. Potential applications of this study include demonstrating glacier recession, helping with mapping snow cover and mass balance, and allowing detailed changes in remote locations to be observed.