Decision support systems (DSSs) have evolved from data- and model-based tools into ‎increasingly intelligent, knowledge-driven, and context-aware systems, yet the field still lacks an ‎integrated representation of its historical development, conceptual hierarchy, and relational ‎organization. This study mapped DSS knowledge from 1980 to 2025 by integrating systematic ‎review, descriptive bibliometric analysis, structured qualitative content analysis, and weighted ‎co-occurrence network analysis. After systematic screening and quality appraisal, 644 English-‎language publications were analyzed. Hierarchical coding revealed a multilevel conceptual ‎structure comprising major and subordinate themes, while network reconstruction identified a ‎broad set of interconnected active concepts. Decision-making processes and phases, DSS ‎components, and technical and operational applications formed the dominant conceptual axes. ‎The network was dense but structurally unequal, and null-model comparisons indicated that ‎high clustering was largely attributable to degree heterogeneity, whereas the Borgatti–Everett ‎core–periphery pattern remained stronger than its degree-preserving reference. Artificial ‎intelligence, machine learning, and explainability occupied comparatively peripheral positions, a ‎pattern more consistent with their relative novelty and specialized linkages than with limited ‎scientific importance. The study provides an integrated multilayer representation of DSS ‎knowledge connecting the knowledge core, application and deployment, foundations and ‎structure, and transformation and development, thereby offering a basis for identifying ‎conceptual gaps and directing future research.‎