The principles of quantum mechanics will enable quantum computing to tackle complex computational problems that can revolutionize earth sciences. It will discuss the prospect of quantum algorithms for major areas such as climate modeling, geophysics, environmental monitoring, and remote sensing in those areas where classical computing could not cope with large complex datasets at the level of precision or speed required. Section 1 illustrates qualitative introduction to quantum computing—the causes of computational bottlenecks in the prevailing earth sciences methods. Another area that goes further into quantum computing’s real-world applications is greater precision in climate simulation, forecasting subsurface exploration, and pollution modelling. This could eventually be reduced to more complex patterns that recognize the structures in data allowing quantum machine learning to develop insight for detecting patterns in greater datasets for more accurate predictions. Using several case studies quantum-enhanced earthquake prediction and climate modeling this chapter is applied towards real-world applications and discusses the current trends in this research area. The topic thereby illustrates the promises and challenges such as the limitations of quantum hardware, integration with classical systems, and scalability for large-scale applications in earth sciences.