Abstract: To improve the safety during highway operation, it is crucial to accurately analyze and assess the displacement status and deformation trend of highway slopes. A slope deformation prediction method based on multi-sensor information fusion technology is proposed here. Firstly, displacement data collected by an inclinometer and global navigation satellite system (GNSS) at specific locations on the highway is pre-processed to remove noise and other interference. Subsequently, the processed data are fused and employed for training the long short-term memory (LSTM) neural network model. Then, the trained model is applied to predict the future deformation trend of the slope, enabling effective analysis and prediction of the slope displacement. By comparing actual monitoring data with predicted data, the reliability of the slope deformation prediction model is verified. Corresponding results show that the prediction model can accurately reflect the real situation and trend of slope displacement. This confirms that the slope deformation prediction method based on multi-sensor information fusion technology and LSTM neural network can provide robust support for highway slope safety monitoring and early warning, which is significant for ensuring highway operation safety.