Every day, data processing becomes increasingly important. It's vital to use high-performance computing to process such big data. There are billions of spatial points in Geographic Information Systems (GIS) to be managed within a reasonable period. One of the basic operations is to prepare triangulation data. This study proposed and implemented methods to produce Parallel Delaunay Triangulation for Large Data in Geographic Information System. Our proposed approach is based on the Divide and Conquer algorithm. The set of points in the regions can be divide into independent partitions, and each partition is separately triangulated. Lastly, we used stitching methods to merge these regions into a single result. In our implementation, we use C++ and MPI to evaluate our algorithm.