This thesis explores the application of machine learning algorithms for detecting fairy circles, which are circular depressions linked to natural hydrogen emissions, using satellite imagery. As hydrogen becomes a crucial alternative energy source, identifying natural hydrogen emission sites is vital for sustainable energy exploration. Fairy circles, found in many regions around the world, serve as indicators of probable hydrogen-rich areas. This research employed object detection techniques in ArcGIS Pro and Custom Vision to develop a method for automatically discovering these formations. The results of the study showed that Custom Vision produced promising outcomes, particularly in terms of recall, which was high throughout the three performed iterations, indicating that most of the potential fairy circles were detected. However, precision remained a challenge, creating a significant number of false positives. On the other hand, the object detection attempts in ArcGIS Pro were less successful. The models trained with the Single Shot Detector (SSD) architecture failed to produce accurate results, accounting to average precision scores of zero during the three deep learning attempts. Issues included poor resolution of the non-commercial satellite imagery, highly heterogeneous terrain with many anthropogenic features, and difficulties in detecting smaller (less than 100 meters) or partially obscured circles. Surface roughness analysis using averaged SAR backscatter and backscatter change imagery highlighted alterations in vegetation patterns around and within the circular structures believed to be fairy circles. Averaged backscatter images, coupled with log-based maps, revealed that some of the potential fairy circles exhibited dynamic vegetation patterns, which may differ from the conventional idea of decreasing vegetation in the center of the formations and increasing growth around the margins. Historic Wayback images were used to understand the vegetation changes. The study also made use of spectral indices, such as Normalized Difference Built-up Index (NDBI) and Soil-Adjusted Vegetation Index (SAVI), to analyze a potential connection between the fluctuations in indices¿ values and highly variable hydrogen soil content typically observed in fairy circles. Despite the challenges, this research demonstrates the possibilities of machine learning and remote sensing technologies in supporting natural hydrogen exploration, which can be further explored in future studies.