Monitoring rainfall is essential to understanding hydrological processes, managing water resources, and mitigating drought and flood risks. Many regions, particularly in developing countries, have sparse rain-gauge networks, limiting the spatial coverage and resulting in inaccurate rainfall estimates. By combining remote sensing data with rain-gauge measurements, rainfall estimates can be improved, and spatial coverage can be enhanced. Remote sensing techniques provide a valuable resource for supplementing and enhancing rainfall monitoring in such areas. This study leverages Global Precipitation Measurement (GPM) satellite data to enhance rainfall estimation in White Nile State, Sudan, where only two rain-gauge stations are operational; the state's total area is 39.600 Km². GPM data, well known for its high temporal and spatial resolution, offer a promising alternative to mitigate the limitations of sparse ground-based networks. The study integrates GPM satellite data with ground-based measurements through statistical and geostatistical techniques, and validation, to improve rainfall accuracy. The results indicate that GPM data effectively complement rain-gauge observations, capturing spatial rainfall patterns and extreme events more accurately. The findings underscore the potential of remote sensing to provide reliable rainfall information in data-scarce regions, contributing to better water resource management and disaster risk reduction strategies.