With the increasing urgency to address global environmental challenges, the pursuit of sustainable solutions has become a priority across various sectors, including engineering. Eco-innovation, aiming to integrate environmentally responsible and economically viable practices in industrial processes, is fundamental within this context. Industrial eco-innovation is crucial for addressing contemporary environmental and economic challenges, promoting a transition to a more sustainable production model. Decision support systems based on fuzzy logic emerge as promising tools to assist in selecting and implementing eco-innovation strategies due to their ability to handle uncertainty and imprecision, addressing the complexities of sustainability. This research aims to explore the application of fuzzy decision support systems in promoting eco-innovation strategies within Sustainable Systems Engineering. Through this approach, we seek to advance more sustainable practices and construct environmentally responsible and economically viable engineering systems. To measure the level of eco-innovation, an index has been created with the following terms: Alarming [0,1.5], Insignificant [1.5,3.5], Moderate [3.5, 6.3], Adequate [6.3, 8.5], and Promising [8.5, 10]. This scale allows for a comprehensive evaluation, reflecting on the worst-case scenarios to the most promising ones, in terms of environmental impact and sustainability. The analysis of the eco-innovation index reveals a deeper understanding of the different levels of environmental impact and sustainability in various industrial contexts, from "Alarming" to "Promising". This enables the identification of critical areas that require immediate interventions and recognition of strengths and opportunities for improvement in existing processes. Incorporating fuzzy logic into decision support systems for eco-innovation is a notable leap in research in Sustainable Systems Engineering. This flexible method enhances the management of sustainability challenges by addressing complexity and uncertainty, fostering informed decision-making and streamlined eco-innovation strategies.