This paper introduces an enhanced algorithm for segmenting point clouds into clusters based on Euclidean distance. By considering Euclidean distance— a simple and powerful geometric measure—we introduce a new method that not only outperforms previous methods in segmentation accuracy but also handles the scale sensitivity issue of existing Euclidean distance-based work. Our experiments demonstrate that our improved algorithm has a greater ability to accurately segment complex point cloud data, representing significant progress in the algorithm and the application of the field. By exploring segmentation process’s different approaches and presenting an advanced algorithm, this paper makes a contribution to the evolution of point cloud processing, providing valuable knowledge and tools for both researchers and practitioners. Results demonstrate that the performance of clustering is enhanced by the proposed algorithm as opposed to the other algorithms.