This study presents an integrated decision-support framework that combines fuzzy logic with life cycle assessment (LCA) to evaluate sustainable municipal solid waste management (MSWM) strategies under data uncertainty, using Delhi as a representative metropolitan context. Seven MSWM scenarios, from conventional composting and incineration to hybrid systems involving anaerobic digestion and mechanical-biological treatment (MBT) with energy recovery, were modeled using fuzzy triangular numbers applied to key parameters such as waste composition, treatment efficiency, and emissions. A fuzzy-TOPSIS method was employed to rank scenarios based on environmental and operational performance indicators. The results indicate that scenarios involving MBT with energy recovery and anaerobic digestion outperform traditional greenhouse gas reduction, energy yield, and landfill diversion methods. Waste quantity was found to have a more significant impact on system performance than treatment capacity, highlighting the model's sensitivity to real-world variability. Although the study focuses on a single urban region and incorporates expert-derived fuzzy ranges, its methodological framework is adaptable to similar urban contexts. By embedding fuzzy logic into LCA for urban MSWM, the research addresses a critical gap in current modeling approaches. It offers a novel, uncertainty-resilient tool for municipal planners and environmental policymaker.