Recent progress in remote sensing has snapshotted unprecedented numbers of river planform geometry, providing opportunity to revisit the oversimplified channel shape parameterizations in global hydrologic models. This study leveraged two recent Landsat‐derived global river width databases and created a reach‐level width dataset to measure the validity of model parameterizations at ~1.6 million kilometers of rivers in length. By showing state‐of‐the‐art parameterization schemes only capture 30–40% of the width variance globally, we developed a machine learning (ML) approach surveying 16 environmental covariates, which considerably improved the predictive power (R² = 0.81 and 0.77 for two testing cases). Beyond the commonly discussed upstream basin conditions, ML revealed that local physiographic factors and human interference are also important covariates for width variability. Finally, we applied the ML model to estimate bankfull river width, creating a new reach‐level dataset for use in global hydrodynamic modeling.