Water vapor is an important part of the atmosphere, and it is of great significance to realize the high-precision retrieval of water vapor content in the atmosphere for meteorological research. This paper used brightness temperature data of FY-3D/MWRI in July from 2019 to 2022 annually, with the water vapor data of ERA5 on Pacific as references, established six-channel and eight-channel random forest retrieval models (RF6 and RF8) for total precipitable water in maritime clear sky, based on the random forest algorithm. The experimental results indicated that compared to the empirical regression retrieval model, the random forest retrieval model have improved accuracy obviously, with the RF6 model achieving an improvement of about 22% and the RF8 model achieving an improvement of about 28%. Furthermore, when applying the RF6 and RF8 models which trained based on Pacific region data to the North Atlantic and South Indian Ocean, positive retrieval results were also obtained. Considering all factors, the RF8 model outperforms the RF6 model, and the RF6 model outperforms the empirical regression model.