The rapid growth of municipal solid waste (MSW) in urbanizing regions has intensified the need for scientifically robust and policy-relevant frameworks for landfill site selection. Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approaches have been widely adopted to support this process. However, existing reviews largely emphasize method classification and application trends, with limited critical evaluation of validation practices, uncertainty handling, and decision transparency. This study presents a structured analytical synthesis of 211 peer-reviewed studies published between 2005 and 2025. The review examines the evolution of GIS-MCDM frameworks from conventional AHP-based models to fuzzy, hybrid, and AI-assisted approaches, with a focus on weighting strategies, aggregation methods, and validation practices. The findings show that AHP-based methods remain dominant due to their transparency and ease of implementation, but often lack robustness in handling uncertainty and complex spatial interactions. To address these limitations, the study proposes an integrative conceptual framework that emphasizes systematic validation, hybrid and objective weighting techniques, and the incorporation of explainable artificial intelligence to improve transparency and reproducibility. By linking methodological developments with planning implications, this review provides a clear analytical basis for enhancing the reliability and policy relevance of GIS-MCDM approaches in sustainable landfill site selection.
Kumar et al. (2026) studied this question.