<![CDATA[Decision Support Systems (DSS) increasingly utilize Multi-Criteria Decision Making (MCDM) methods to address complex decision problems involving multiple and often conflicting criteria. Among these methods, the AHP–TOPSIS hybrid approach has gained significant attention due to its ability to combine structured criteria weighting with objective alternative ranking. However, existing studies are generally fragmented and domain-specific, limiting a comprehensive understanding of its application trends and methodological patterns. This study aims to provide a systematic synthesis of AHP–TOPSIS applications in DSS through a Systematic Literature Review (SLR). The review follows PRISMA guidelines and analyzes 20 journal articles published between 2014 and 2025, sourced from Google Scholar, Garuda, and SINTA databases, with limited support from Scopus-indexed references. The results show a notable increase in publications after 2020, indicating the growing importance of data-driven and transparent decision-making in the context of Industry 4.0 and 5.0. The majority of studies focus on selection and ranking problems, particularly in education, human resource management, and industrial applications. A consistent methodological pattern is observed, where AHP is used for determining criteria weights and TOPSIS is applied for ranking alternatives. This study contributes by providing a cross-domain perspective on the use of AHP–TOPSIS, highlighting its methodological consistency, adaptability, and practical relevance. The findings also suggest opportunities for future research, particularly in integrating AHP–TOPSIS with advanced approaches such as fuzzy logic and intelligent systems to address uncertainty and dynamic decision environments]]>
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