The importance of corporate reputation is a critical issue for business growth, sustainability, and success, as it represents a key intangible asset for the management of all companies. This business importance has its correlation in the academic and research field, where corporate reputation has a high number of publications in the literature. However, despite the importance of this concept, one of the great challenges of recent decades, and one that is still evident today, is how to measure corporate reputation quantitatively and how it affects sustainability. Following an in-depth exploration of the available literature, this manuscript aims to demonstrate the effective application of fuzzy models in enhancing decision-making processes within the realm of corporate reputation management for companies. To achieve this goal, this paper proposes a new corporate reputation measuring model based on the fuzzy 2-tuple linguistic and AHP (Analytic Hierarchy Process) methodologies. The proposed model promotes the computation of corporate reputation for companies based on three widely cited and universally recognized criteria outlined in the literature, drawing inspiration from a well-established framework in the field. This approach ensures a comprehensive and widely accepted foundation for evaluating corporate reputation: Capability, Benevolence, and Integrity and adding the Net Promote Score variable. To integrate sustainability into this equation, our model suggests the inclusion of variables related to sustainable practices in the measurement of corporate reputation. Recognizing the growing importance of sustainability in the public perception of companies, factors such as social responsibility, environmental management, and business ethics are recommended for consideration in the assessment of corporate reputation. The model proposed in this paper is tested and validated on a real business case, based on the selection of several companies selected for an empirical study in the selection of suppliers. For future research endeavors, the authors suggest expanding the model to encompass various decision-making processes. Additionally, they recommend exploring the integration of machine learning algorithms and data analysis techniques to identify patterns and provide recommendations for enhancing corporate reputation.