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A bibliometric analysis of multi-criteria decision-making (MCDM) studies was conducted using 8,947 articles published between 1995 and 2025. Only studies indexed in the Web of Science Core Collection (SCI-Expanded and SSCI) were considered. An adapted PRISMA protocol and bibliometric methods were applied using VOSviewer and Biblioshiny. The analyses include descriptive statistics, life-cycle modelling, co-citation and bibliographic coupling, keyword co-occurrence, thematic mapping, collaboration networks, and citation structure. The results reveal three growth phases, with acceleration after 2016 and a compound annual growth rate of 21.82% over 1995–2025 (7.4% over 2020–2025). A logistic model fits the trend well (R² = 0.991), indicating 90% of estimated saturation around 2032 (95% bootstrap interval: 2031–2034) if the post-2022 slowdown continues. This estimate is treated as a scenario rather than a forecast. The field's intellectual framework is based on fuzzy set theory and MCDM methods, particularly TOPSIS, AHP, and VIKOR, while its conceptual space has diversified toward sustainability, artificial intelligence, and engineering applications. Collaboration and citation networks reveal a globally distributed but fragmented community, with contributions from China, Türkiye, India, and Iran, and institutional centers in Eastern Europe and Asia. Productivity, citation impact, and collaboration centrality are moderately correlated, indicating that prolific contributors are not necessarily the most cited or central. Web of Science categories confirm the field's interdisciplinary position across environmental sciences, computer science, and operations research. Research gaps include the dominance of five classical methods (92.6% of articles), limited research on rank reversal (1.1%), weak integration of healthcare and agri-food applications into their home disciplines, insufficient attention to digital transformation and the circular economy, and geographical concentration, with four countries producing 55.5% of corresponding-author output. These findings provide a data-driven overview of the field's development, current state, and future directions.
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Şahin et al. (2026) studied this question.
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