University of Birjand-Research Group of Drought and Climate Change Journal of Drought and Climate change Research 3092-6076 Articles in Press 2026 05 24 Mapping a Quarter-Century of Fuzzy-Based Models for Groundwater Level Estimation: A Bibliometric Analysis Mapping a Quarter-Century of Fuzzy-Based Models for Groundwater Level Estimation: A Bibliometric Analysis 3986 10.22077/jdcr.2026.10852.1207 FA Sepide Zeraati Neyshabouri Ph.D. Candidate in Water Resources Department of Water Engineering University of Birjand, Birjand, Iran. 0000-0003-4691-8194 Abbas Khashei Siuki 2. Professor, Department of Water Science and Engineering, University of Birjand, Birjand, Iran* 0000-0002-2863-8482 Mohammad Ghasem Akbari Department of Statistics, University of Birjand, Birjand, Iran 0000-0001-5064-8065 Journal Article 2026 01 05 Groundwater, the planet’s largest freshwater reservoir, faces mounting stress from overuse and climate change, demanding interpretable, uncertainty-aware modeling tools. Fuzzy-based modeling offer unique solutions, yet their global research landscape, scientific evolution, intellectual structure, and global diffusion remains unmapped. To address this gap, we present the first focused bibliometric synthesis of fuzzy-based approaches for groundwater level estimation, analyzing 189 Web of Science–indexed articles published between 2000 and 2025 using Bibliometrix and VOSviewer. Our analysis quantifies publication growth, identifies leading countries, institutions, and authors, and maps thematic clusters. Results reveal a 19.8% annual growth rate since 2016, with Iran dominating in publications and the United States achieving the highest citations. Key institutions include Islamic Azad University, University of Tabriz, and University of Tehran, while leading authors such as Kisi, El-Shafie, and Nourani anchor the intellectual structure of the field. Science-mapping demonstrates the dominance of hybrid frameworks, particularly those integrating wavelet transforms, metaheuristics, or adaptive neuro-fuzzy inference systems (ANFIS). Keyword co-occurrence networks (minimum occurrence = 5) and temporal overlay visualizations demonstrate a paradigm shift from standalone fuzzy systems toward climate-aware, physics-informed hybrids. This study consolidates a scattered body of knowledge and establishes fuzzy-based groundwater modeling as a maturing sub-discipline. We identify critical frontiers including explainable AI integration, improved spatial scalability, and applications in data-scarce transboundary aquifers. Future research should prioritize cross-paradigmatic collaboration to bridge data-driven and process-based modeling, enhancing both scientific rigor and policy relevance, particularly in the context of escalating water insecurity driven by climate change. Groundwater, the planet’s largest freshwater reservoir, faces mounting stress from overuse and climate change, demanding interpretable, uncertainty-aware modeling tools. Fuzzy-based modeling offer unique solutions, yet their global research landscape, scientific evolution, intellectual structure, and global diffusion remains unmapped. To address this gap, we present the first focused bibliometric synthesis of fuzzy-based approaches for groundwater level estimation, analyzing 189 Web of Science–indexed articles published between 2000 and 2025 using Bibliometrix and VOSviewer. Our analysis quantifies publication growth, identifies leading countries, institutions, and authors, and maps thematic clusters. Results reveal a 19.8% annual growth rate since 2016, with Iran dominating in publications and the United States achieving the highest citations. Key institutions include Islamic Azad University, University of Tabriz, and University of Tehran, while leading authors such as Kisi, El-Shafie, and Nourani anchor the intellectual structure of the field. Science-mapping demonstrates the dominance of hybrid frameworks, particularly those integrating wavelet transforms, metaheuristics, or adaptive neuro-fuzzy inference systems (ANFIS). Keyword co-occurrence networks (minimum occurrence = 5) and temporal overlay visualizations demonstrate a paradigm shift from standalone fuzzy systems toward climate-aware, physics-informed hybrids. This study consolidates a scattered body of knowledge and establishes fuzzy-based groundwater modeling as a maturing sub-discipline. We identify critical frontiers including explainable AI integration, improved spatial scalability, and applications in data-scarce transboundary aquifers. Future research should prioritize cross-paradigmatic collaboration to bridge data-driven and process-based modeling, enhancing both scientific rigor and policy relevance, particularly in the context of escalating water insecurity driven by climate change. https://jdcr.birjand.ac.ir/article_3986_540393ae7f8b7a7fd6cdf47250b05679.pdf