To investigate the temperature patterns of steel box girders in long-span cable-stayed bridges on high-speed railways, this study utilized measured temperature data from the Yuxi River Bridge on the Shangqiu-Hefei-Hangzhou High-Speed Railway, along with database resources. By employing machine learning techniques, the research explored the influence of various meteorological factors on the temperature behavior of steel box girders, as well as the temporal and spatial distribution characteristics of the temperature field. By establishing machine learning mo-dels that map various meteorological factors to the uniform temperature of the steel box girder, the superiority, inferio-rity, and applicability of each model were analyzed, and the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder was obtained. A comprehensive study on the vertical distribution pattern of the temperature of the steel box girder was conducted using machine learning methods and exponential fitting. The results show that the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder from high to low is: air temperature, cumulative radiation, air pressure, humidity, radiation intensity, wind direction, horizontal visibility, wind speed, and precipitation, with the temperature importance far exceeding other meteorological factors. Among them, the atmospheric temperature 2 to 3 hours ago has the greatest impact on the uniform temperature of the steel box girder, reflecting a lag of 2 to 3 hours in the impact of atmospheric temperature changes on the uniform temperature of the steel box girder. Neural networks, random forests, and XGBoost models can all accurately predict the uniform temperature of the steel box girder, with the neural network model performing better overall. The negative temperature gradient in the steel box girder exhibits lower sensitivity to meteorological factors and is more strongly correlated with the internal heat transfer characteristics of the structure itself. The exponential function can accurately fit the vertical distribution of the maximum positive temperature gradient in steel box girders, with its parameters determinable through machine learning methods. Each parameter holds distinct physical significance. The research findings provide valuable reference for predicting temperature fields and understanding distribution patterns in the steel box girders of long-span cable-stayed bridges on high-speed railways.