Traditional methods for monitoring greenhouse gas fluxes at the water-air interface in reservoirs have many limitations. The analysis on its influencing factors is also mainly based on mathematical statistics. This study provides an innovative approach by using machine learning algorithms. In this study, two traditional machine learning algorithms (Random forests (RF) and Support vector machine (SVM)) and two deep learning algorithms (Convolutional neural network (CNN) and long and short term memory neural network (LSTM)) were applied to predict CO₂ and CH₄ diffusion fluxes. In addition, the feature importance assessment in RF and the decision tree (DT) are used to analyze the relationship between environmental factors and GHG diffusion fluxes in reservoirs from a new perspective. The results showed that deep learning produced excellent prediction accuracy, whereas prediction accuracy of RF was significantly better than SVM in traditional machine learning. LSTM and RF yielded optimal accuracy in predicting CO₂ flux and CH₄ flux, respectively. The root mean square error (RMSE) was 0.424 mmol/(m²·h) and 0.140 μmol/(m²·h) and R² of the predicted and measured values were 0.960 and 0.758, respectively. RF identified sediment and nutrient as critical environmental factors to GHG fluxes, followed by climate factors and water environment factors. Lastly, a decision tree was used innovatively to depict the limiting threshold of environmental factors that determines the source or sink of CO₂. The classification accuracy of this decision tree is as high as 100% in this study. The results of decision tree also showed that low dissolved inorganic carbon concentration and alkaline conditions are favorable for water to absorb atmospheric CO₂. These results demonstrate the great potential of using machine learning algorithms to predict and analyze GHG fluxes at the water-air interface in reservoirs.