Accurate prediction of the excavator bucket's landing point is a critical aspect of achieving intelligent operation. However, existing prediction methods generally suffer from low computational efficiency, vulnerability to environmental factors, and poor feasibility, making them inadequate to meet the dual demands of efficiency and prediction information completeness required for engineering construction. To address these issues, this paper proposes a bucket landing point prediction method that integrates point cloud information. First, IMU data is filtered, and a kinematic model of the working device is established to calculate the bucket's pose information. Then, LiDAR point cloud data is processed, and dynamic region point clouds are extracted based on the bucket's pose, followed by the reconstruction of the elevation contour, on which the key point distances are calculated. Finally, various experimental scenarios were constructed in Gazebo to comprehensively validate the proposed method through simulation. The results demonstrate that the proposed method meets the requirements for real-time and complete information in predicting the excavator bucket's landing point.