The global mean sea level is rising at an average rate of 4 millimeters per year, posing a significant threat to coastal communities and global ecosystems. The increase in ice discharge from glaciers worldwide, and in both the Greenland and Antarctic ice sheets, is now the dominant source of global mean sea level rise. However, its dynamic response to climate change remains a fundamental uncertainty in sea level rise projections⁠. This mini-symposium will feature presentations on studies that leverage computational mechanics, high-performance computing, and scientific machine learning along with remote sensing data to better predict mass loss from glaciers and ice sheets for global mean sea level rise projections. Relevant topics include: (i) computational strategies and software for tackling the complex, non-linear, multi-scale, multi-physics problems arising in ice sheet modeling, (ii) advanced analysis techniques, including approaches for model initialization/calibration and uncertainty quantification, (iii) performance portability of ice sheet models to advanced heterogeneous architectures, and (iv) approaches for improving the accuracy and/or efficiency of ice sheet models through the integration of AI/ML.