The ongoing advancement of cascade reservoirs has resulted in the formation of supersaturated total dissolved gases, which are challenging to disperse in riverine environments. This phenomenon has the potential to precipitate the onset of gas bubble disease and, in extreme cases, may even result in fish mortality. Therefore, the development of a predictive model for total dissolved gases downstream of dams is important for biodiversity conservation. This paper collected data from three dam monitoring stations on the Columbia River in the United States, comprising measurements of water temperature, barometric pressure, flow, dam overflow, and total dissolved gas saturation. These data were used to train three machine learning algorithms, namely, BP neural networks, random forests, and boosting trees, which were then employed to predict total dissolved gas saturation. The performance of the three algorithms was evaluated and compared. It found that as the number of significantly correlated input variables increases, the predictive performance of each model showed an upward trend, and different models were affected differently by input factors. Random forest (MAE=1.273%, RMSE=1.775%, R²=0.952) and boosting tree (MAE=1.268%, RMSE=1.622%, R²=0.960) had the best prediction performance under the optimal input variable scheme. In the model validation phase, boosting tree and random forest showed higher accuracy, with average relative errors of 2.3% and 2.6% between their predicted and measured values. In the model validation phase, the boosting tree model can control the average relative error between the predicted and measured values within 2.4%. The model constructed in this study can rapidly and accurately predict total dissolved gas (TDG) saturation in the downstream channel during dam releases. This enables the risk of TDG saturation to be assessed in advance, thereby facilitating the timely adjustment of the discharge scheduling scheme. Protective measures are taken in advance for localized fish sanctuaries, thus reducing the impact of TDG on aquatic ecology. The results of the study can provide some reference value for in-depth machine learning-based total dissolved gas prediction modeling.