As a promising new energy resource, natural gas hydrates are important for energy exploration and development by studying their geological distribution. The rapid growth of big data technologies has provided new perspectives and technical support for this field in recent years. This paper systematically reviews the application of big data technologies in the study of natural gas hydrate geological distribution, covering recent advancements in data integration and management, machine learning, and predictive modeling, as well as spatiotemporal analysis and visualization. By analyzing case studies from typical global hydrate distribution regions, the paper highlights the effectiveness and potential of big data technologies in predicting and validating hydrate distribution. Addressing key challenges in current research, such as data acquisition, quality control, and multidisciplinary collaboration, the paper proposes potential solutions and outlines future technological development directions. This paper aims to provide a reference for advancing research on the geological distribution of natural gas hydrates and to support scientific decision-making in energy development and utilization.