<p>Industrial manufacturing plants host numerous physical assets, some of which pose substantial sustainability risks across economic, social, and environmental dimensions. This study aims to develop a structured methodology for identifying, prioritizing, and mitigating sustainability challenges at the asset level. A set of sustainability impact criteria was first systematically selected and validated through expert assessments, ensuring relevance and practicality. The proposed approach utilizes readily available operational data from production, maintenance, health and safety, quality, emissions, and waste management records. A balanced framework is constructed using the fuzzy analytical hierarchy process (AHP), converting subjective expert evaluations into objective weighted factors that assess the relative importance of various sustainability criteria. By integrating operational performance metrics with expert insights, a single composite impact score (CIS) is formulated to enable asset ranking and prioritization. Case studies demonstrate that the framework effectively ranks assets based on sustainability risks, revealing critical areas requiring intervention. Proactive maintenance strategies informed by this approach enhance operational resilience and sustainability performance. The study provides practitioners with an actionable tool for data-driven decision-making, optimizing resource allocation, and aligning maintenance practices with sustainability goals. For researchers, it offers a validated methodology for assessing multi-dimensional sustainability impacts at the asset level. This work contributes the first integrated framework combining fuzzy AHP with operational data analytics to quantify and manage asset-level sustainability risks, filling a critical gap in industrial sustainability management.</p>