Identifying a central node within a set of distributed geo-locations is a fundamental problem in spatial analysis, logistics, and network optimization. This research presents an algorithmic framework for Central Node Detection in Multi-Coordinate Systems Using Distance Minimization. The proposed approach integrates data from heterogeneous coordinate systems—such as geographic (latitude–longitude) and Cartesian (x, y, z)—into a unified spatial reference model. After normalization, a distance-based minimization algorithm is employed to determine the point that minimizes the total or average distance to all given locations, effectively identifying the most central node. Both Euclidean and great-circle distance metrics are analyzed to ensure adaptability to planar and spherical data domains. The model is designed to handle large-scale, multi-source datasets with varying spatial precision. Experimental evaluations demonstrate that the proposed method achieves high accuracy and computational efficiency compared to traditional centroid and geometric median approaches. The results highlight its applicability in network design, logistics hub placement, sensor network optimization, and geo-spatial clustering, providing a robust foundation for centralized decision-making in complex spatial systems.