To mitigate climate change by reducing CO₂ emission，carbon capture，utilization，and storage （CCUS） technologies have garnered significant attention. However，the required large scale investment and inflexibility of CCUS projects have greatly hindered their widespread applications. In this context，systematic source-sink matching has emerged as a key research focus，as scientific and efficient matching can optimize pipeline network design and reduce the overall costs of CCUS implementation. To this end，this study proposes a CCUS pipeline network layout optimization method using Density-Based Spatial Clustering of Applications with Noise （DBSCAN），offering a solution for CCUS pipeline network design. Firstly，the DBSCAN clustering is employed to cluster emission sources and storage sinks. Subsequently，a CCUS source-sink matching model is developed based on the minimum spanning tree method after comprehensively considering source-sink characteristics and cost components across operational phases to generate theoretical CCUS matching schemes. Finally，to address pipeline redundancy caused by multi-sources to one-sink configurations，an improved saving algorithm is applied to optimize the CCUS source-sink matching scheme. A hypothetical planning region is presented for testing of the proposed method，which demonstrates that the model does not only reduce deployment costs but also significantly shortens transportation distances. Compared with traditional methods，the total deployment costs decrease from 130 billion CNY to 98 billion CNY，by a reduction of approximately 24.6%，while the transportation distance is reduced from 4 075 km to 1 008 km，marking a decrease of 75.3%. These findings validate the adaptability and economic efficiency of the proposed method in complex CCUS scenarios，and the proposed method provides a feasible optimization pathway and theoretical foundation for CCUS system planning.