Abstract: The calculation of atmospheric transmittance is the core of solving emitted infrared radiative transfer equations. As the layer transmittance of the atmosphere is parameterized by functions of the mean layer temperature, water vapor, ozone and other gas concentrations, RTTOV (Radiative Transfer for TOVS) is able to quickly and accurately compute level-to-space transmittance and top-of-atmosphere radiance. However, large computing errors have been found in some strong absorption bands, for example, the water vapor band. To solve this problem, a machine learning method called Gradient Boosting Tree (GBT) is utilized to compute transmittances in this paper. 1406 typical profiles have been selected from the ECMWF IFS-137 (European Centre for Medium-Range Weather Forecasts, the Integrated Forecast System, 137-level-profile) as training samples. The water vapor channel (7.33 μm) of the IRAS (InfraRed Atmospheric Sounder)/FY-3 (Fengyun 3 series) is selected for a case study. A fast model to compute clear-sky level-to-space transmittance and brightness temperature by the GBT method (GBT model hereafter) has been built. The calculated transmittances and brightness temperatures have been validated by the ground-truth. The comparison results show that the mean absolute errors (MAE) of clear-sky transmittances calculated by the GBT model are 0.0012 (land) and 0.0009 (ocean), while the mean squared logarithmic errors (MSLE) are 0.0215 and 0.0095 over land and ocean, respectively. These results are smaller than those calculated by RTTOV (MAEs of 0.0008 (land) and 0.0010 (ocean), MSLEs of 0.0135 (land) and 0.0227 (ocean)). In addition, the MAEs of clear-sky BT calculated by the GBT model are 0.0949 K (land) and 0.0634 K (ocean), and the root mean square errors (RMSE) are 0.1352 K (land) and 0.0831 K (ocean), respectively, which are smaller than those by RTTOV (MAEs of 0.1685 K (land) and 0.1466 K (ocean), RMSEs of 0.1794 K (land) and 0.1685 K (ocean)). The case study in this paper demonstrates that in the water vapor band of IRAS, the transmittance and brightness temperature predicted by the GBT are more accurate than those simulated by RTTOV. Machine learning method has the potential to improve the accuracy of the transmittance and brightness temperature in the water vapor band. It provides an optional solution to the fast radiation transfer calculation.