Landfill site selection is a complex spatial decision-making challenge involving consideration of many factors, including environmental protection, hydrological safety, engineering feasibility, social acceptance and regulatory compliance. Over the past 2 decades, Geographic Information Systems (GIS) combined with Multi-Criteria Decision-Making (MCDM) techniques have emerged as the dominant framework for evaluating landfill suitability. However, existing reviews largely focus on bibliometric trends, leaving a critical gap in conceptual and methodological synthesis. This review systematically examines studies published between 2005 and 2025 to evaluate the theoretical foundations, analytical structures, uncertainty-handling capabilities and practical implications of classical, fuzzy, hybrid and artificial intelligence/machine learning (AI/ML)–based GIS-MCDM approaches. Classical techniques (Analytical Hierarchy Process (AHP), Weighted Linear Combination (WLC)) and structured decision logic with fuzzy methods provide interpretability and enhance representation of linguistic uncertainty; hybrid frameworks integrate subjective and objective weighting to improve robustness; and AI/ML methods offer nonlinear modelling, predictive suitability mapping and data-driven weight derivation. To address inconsistencies in criteria selection, a harmonized framework encompassing environmental, topographical, land-use, infrastructural, climatic and socio-economic-regulatory dimensions is proposed. An integrated GIS-MCDM workflow demonstrates how preprocessing, standardization, weighting, overlay modelling and validation interact within a unified decision-support system and the applicable regulatory policy. This review further synthesizes recurring limitations and outlines strategic directions for future research, including spatiotemporal integration, advanced uncertainty modelling, regional-scale assessments and adoption of transparent, reproducible GIS workflows. Overall, this study provides a conceptual and methodological foundation to guide method selection, enhance analytical rigor and strengthen the scientific basis for sustainable and socially equitable landfill planning.
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