The elevation datum is a critical element in surveying and mapping, as variations in elevation systems can lead to discrepancies between Digital Surface Model (DSM) products generated from satellite imagery. To eliminate these differences and ensure high-precision data consistency, this study constructs an elevation datum conversion scheme for multi-source DSM products using the SGG-UGM-2 (2190 degree) global gravity field model to calculate elevation anomalies. While traditional serial algorithms suffer from significantly decreased efficiency as the volume of DSM image files increases, this paper proposes a novel HDC-MPI elevation datum conversion algorithm based on Message Passing Interface (MPI) parallel technology. By leveraging distributed memory parallel computing, the processing task is partitioned into multiple sub-tasks, substantially enhancing overall throughput. Experimental results demonstrate that: (1) the HDC-MPI algorithm improves conversion efficiency by approximately 8 times compared to the serial approach when processing 12 image scenes; (2) the algorithm’s efficiency is primarily governed by image memory usage rather than terrain complexity; and (3) the conversion accuracy of the HDC-MPI algorithm remains fully consistent with serial results, ensuring the reliability of the elevation datum transformation.