Accurately retrieving precipitable water vapor (PWV) over wide-area land surface remains challenging. Unlike passive infrared remote sensing, passive microwave (PMW) remote sensing provides almost all-weather PWV retrievals. This study developed a PMW-based land PWV retrieval algorithm using the automated machine learning (AutoML). Data from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) serves as the main predictor variables and high-quality Global Positioning System (GPS) PWV data as the target variable. Unprecedentedly large GPS training samples (over 50 million) from more than 12,000 stations worldwide are used to train the AutoML model. New predictors with clear physical mechanisms enable PWV retrieval over almost any land surface type, including snow cover and near open water. Validation shows good agreement between PWV retrievals and ground observations, with a root mean square error of 3.1 mm. This encouraging outcome suggests that the algorithm’s potential for application with other PMW radiometers with similar wavelengths.