Satellite-driven soil moisture monitoring systems currently available fail to meet the spatial resolution requirement for a wide range of applications. This limitation is particularly critical for agricultural water management, assessing risks associated with extreme events, and hydrological modeling. This work aims to address the spatial limitations of satellite soil moisture remote sensing by developing GSSM-10, a global 10-meter resolution surface soil moisture dataset, using multi-sensor datasets integrated within an ensemble machine learning framework. These datasets encompass diverse data types—active microwave, multispectral, thermal infrared, and land elevation—offering a robust and comprehensive approach to estimating surface soil moisture (SSM). The ensemble model incorporates TabNet, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The model was trained on ground-truth data collected from the International Soil Moisture Network (ISMN). The ensemble model demonstrated robust performance, achieving an R² of 0.8344, a bias of –0.0001, an RMSE of 0.0433 m³/m³, and an ubRMSE of 0.0433 m³/m³ in 5-fold cross-validation. When evaluated on a held-out test set, the model achieved similar levels of accuracy, with an R² of 0.8591, a bias of –0.0002 m³/m³, and an RMSE/ubRMSE of 0.0401 m³/m³. An interactive web platform has been developed for data access, visualization, and download, enabling broad adoption by researchers, practitioners, and policymakers. By providing globally consistent, high-resolution SM estimates, GSSM-10 represents a significant advancement in satellite-based soil moisture monitoring for environmental and agricultural applications.