The terrain in western Sichuan is complex and varied, and the geological structure is active, which makes the construction and maintenance of the traffic trunk line face the challenge of frequent geological disasters. Ensemble learning algorithm can optimize the shortcomings of the algorithm in geological hazard susceptibility assessment and improve the accuracy of the model, which has significant advantages in geological hazard susceptibility assessment. Taking the riverside high-speed as an example, 12 feature variables such as slope and relief were selected to construct the geological hazard susceptibility evaluation system. The forecasting performance of the modeling of the integrated algorithm and a single algorithm was compared and analyzed. The main control factors of the geological disasters along the riverside high-speed were discussed and the practicability of the model was verified. The results show that the proportion of high and extremely high geological hazard prone areas along the Yangtze River high speed is 18.21% and 9.85%, respectively, which are concentrated in the Leibo section and Jinyang section. The area under curve (AUC) of the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve in the integrated model. The AUC of ROC curve (0.84~0.86), the AUC of P-R curve (0.81~0.85) and the F₁ score (0.78~0.79) of the three single machine learning models are significantly higher, and the prediction performance is better than that of a single machine learning algorithm. The development of high-speed geological hazards along the Yangtze River is controlled by topographic and geomorphic factors. The new damage points are located in the highly prone areas of the model, which verifies the accuracy and reliability of the Stacking model.