<![CDATA[This study provides a comprehensive bibliometric analysis of Explainable Decision Support Systems (EDSS) research published between 2010 and 2024, with the objective of mapping its intellectual structure, thematic evolution, and emerging trends. Data were collected from the Scopus database and analyzed using quantitative bibliometric techniques, including performance analysis and science mapping, supported by visualization through VOSviewer. The results indicate a significant growth in EDSS publications, particularly after 2019, driven by the increasing demand for transparency and accountability in artificial intelligence-based decision-making. The network and density analyses reveal that core research themes are centered on machine learning, decision support systems, and artificial intelligence, while emerging topics include interpretability, trust, and human-centered design. Co-authorship and co-citation analyses highlight the interdisciplinary nature of the field, with strong contributions from domains such as healthcare, industry, and data science. Furthermore, the findings demonstrate a shift from purely technical model development toward the integration of explainability, ethics, and user interaction in decision-making systems. This study contributes by offering a structured overview of EDSS research and identifying key directions for future studies, particularly in developing standardized evaluation frameworks and expanding applications beyond dominant domains such as healthcare.]]>
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