Based on machine learning, an application model (GBDT model) of real-time fusion on temperature, precipitation, wind direction, and wind speed at selected locations is developed by using the professional service product (CAR) of Public Meteorological Service Centre of China Meteorological Administration, the multi-source fusion observation analysis data (ART) of National Meteorological Information Centre, the nationwide radar precipitation retrieval product (RAD), the Fengyun-4 satellite precipitation retrieval product (SAT), and the hourly data of nationwide meteorological observation stations. The regional inspection results of the 15-d hourly GBDT fusion product throughout the country are as follows. The GBDT temperature fusion product improves compared to CAR and ART in 6 regions: Northeast China, North China, Northwest China, Central China, Xinjiang, and Tibet, with the most significant improvement in Tibet. In East China and Southwest China, GBDT fusion product is superior to ART, but inferior to CAR, and its error slightly increases compared to ART and CAR in South China and Inner Mongolia. The error of GBDT precipitation fusion product has a slight increase compared to ART and CAR in Inner Mongolia, where there are fewer samples, while in other areas, there are improvements or they are basically equivalent. The GBDT wind speed and direction fusion products have significant improvements compared to ART and CAR. The experiment results indicate that the machine learning method can be applied to fuse multi-source real-time analysis products and observation data, providing real-time meteorological information service of temperature, precipitation, wind direction, and wind speed at selected locations.