To address the challenges of low fusion efficiency, significant stitching errors, and high processing complexity in large-scale point cloud data fusion during collaborative scanning with multiple line laser sensors, this paper proposes a KD-Tree-accelerated data fusion method for multi-line laser sensor systems. The method leverages a dynamic neighborhood search strategy and an adaptive radius adjustment mechanism to enable efficient ordering and parallel smoothing optimization of point cloud data. First, a KD-Tree spatial indexing structure is constructed, and an innovative dynamic neighborhood search strategy is designed to rapidly reorganize disordered 2D contour data into ordered sequences, reducing the algorithm′s time complexity from the traditional O(n2logn) to O(nlogn). Second, by integrating OpenMP multi-threaded parallel computing with an improved Moving Least Squares algorithm, a K-MLS parallel smoothing method is proposed, optimizing the time complexity from O(n2) to O(nlogn), significantly enhancing the processing efficiency for large-scale point clouds. The proposed method was validated in a train wheel measurement system. When processing 2.09 million points, the sorting algorithm achieved a 35.7-fold speedup compared to traditional approaches, while the smoothing algorithm exhibited an 84.5-fold performance improvement. Comparative analysis further demonstrates the method′s effectiveness in improving point cloud quality: it successfully fills data gaps, reduces the maximum deviation in wheel tread measurement from ±0.279 mm to ±0.085 mm, and lowers the mean square error of 3D point cloud registration from 0.323 mm to 0.106 mm. Experimental results confirm that the proposed method maintains sub-millimeter accuracy while significantly boosting processing efficiency for million-scale point clouds. It effectively addresses issues such as stitching errors and uneven point density in overlapping regions during multi-sensor data fusion, proving its robustness and applicability in industrial online measurement scenarios.