Understanding Earth’s magnetosphere and solar activity is critical for forecasting space weather phenomena, which have far-reaching consequences for both technological infrastructure and human activities. The magnetosphere, which acts as a shield against cosmic radiation and the solar wind, is greatly impacted by solar activity, including solar flares and coronal mass ejections. Traditional approaches of researching these complex, interrelated systems typically fall short in capturing their dynamic and nonlinear nature. In Earth sciences, machine learning (ML) has become a vital technique in recent years, providing unparalleled capabilities for analyzing large datasets, uncovering hidden patterns, and developing more accurate predictive models. The importance of applying machine learning techniques to advance our knowledge of solar activity and the Earth’s magnetosphere is covered in this chapter. Researchers can now more accurately anticipate geomagnetic storms, solar flares, and the complicated interactions within the magnetosphere by combining ML algorithms with physical models. Various machine learning methods, including supervised, unsupervised, and deep learning, will be covered in this chapter along with their uses in processing geomagnetic indices, solar measurements, and satellite data in general. It will also cover the problems and constraints of utilizing machine learning in this industry, such as the requirement for high-quality datasets, model interpretability, and domain knowledge integration into ML frameworks. This study offers readers a thorough knowledge of how machine learning (ML) is revolutionizing the study of solar activity and the Earth’s magnetosphere through case studies and real-world applications. It also emphasizes ML’s future potential for improving space weather forecasting, thereby assisting in the protection of both our planet and its technological assets.