This study investigates the application of neural operators, particularly the Fourier Neural Operator (FNO), to enhance the modeling accuracy of the Greenland ice sheet's internal layers. Traditional computational methods often struggle to capture the complex spatial patterns of ice sheets adequately. Our approach leverages a radar image dataset to evaluate the effectiveness of neural operators in comparison to traditional neural network models. The FNO outperformed other models in key performance metrics, such as pixel error and layer accuracy. The results highlight the potential of neural operators to significantly improve the computational efficiency and accuracy of environmental models, paving the way for their broader application in cryospheric science and climate change research.