The forecasting of global natural gas trade flows is of great significance to the stability and development of international energy markets. However, the complex trade environment and real-time data requirements pose great challenges for traditional prediction methods. In order to solve these problems, this study proposes a natural gas trade flow prediction framework that combines 5G communication technology and XGBoost model. 5G communication technology realizes real-time collection and efficient transmission of global natural gas trade data by virtue of its low-latency and high-bandwidth characteristics, which provides high-quality data input sources for the model. At the same time, the XGBoost model shows excellent performance in the processing and prediction of multidimensional complex data by virtue of its powerful nonlinear modeling capability and built-in feature selection mechanism. Experimental results show that compared with traditional regression models and random forest models, the framework achieves significant improvements in indicators such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). In addition, the feature importance analysis reveals that natural gas price and export volume are the key factors affecting trade flows. This study not only verifies the feasibility of the combination of 5G communication technology and XGBoost model, but also provides an innovative technical framework and practical guidance for energy trade forecasting, which has important academic value and application potential.