Environmental, Social, and Governance (ESG) criteria have become essential indicators that not only assess a firm’s sustainability performance but also directly influence long-term financial success, risk management processes, and stakeholder trust. The ESG score presents a detailed analysis of a firm’s environmental, social, and corporate governance factors, serving as a key decision-making tool for investors, regulatory bodies, and other stakeholders. Numerous international organizations and data providers calculate and publish these scores. One such institution, LSEG Data & Analytics, determines ESG scores by applying sector-specific weightings for environmental, social, and governance categories, providing a more tailored evaluation process. However, ESG assessments often involve uncertainties, qualitative data, and subjective judgments, which may limit the effectiveness of traditional evaluation methods. Unlike conventional ESG scoring methodologies, the fuzzy inference approach can more effectively account for intersectoral differences. Instead of assigning separate weightings to the environmental, social, and governance categories, this study proposes a unified rule base that ensures a consistent and adaptable evaluation framework across different industries. This paper presents a fuzzy logic-based inference model to assess the ESG score of firms included within the BIST Sustainability 25 Index.