###### Abstract
Additionally, its computational simplicity is another notable benefit [45; 46; 47]. The Technique for the Order of Preference to Similarity to the Ideal Solution, or TOPSIS method, is another widely recognized MCDM approach used by many researchers [48; 49; 42]. When combined with AHP, the resulting AHP-TOPSIS hybrid method has provided a robust tool that has gained significant popularity among academics in recent years. In this approach, the AHP model first determines the weights of the problem criteria, which are then applied in the TOPSIS model to rank the alternatives. [PERSON] and [PERSON] [46] used the AHP-TOPSIS combination to select learning objects from a variety of options in web-based educational systems, such as texts, data, figures, and tables. [PERSON] and [PERSON] [50] employed the AHP and TOPSIS methods to identify the most beneficial aspects of e-learning during the COVID-19 pandemic. However, some researchers, including [PERSON] et al. [51], have applied alternative algorithms, such as Decision Trees (DT) and Random Forest (RF) Algorithms, to estimate and identify key factors in university students' learning. Fuzzy set theory is a powerful mathematical tool widely used in various scientific fields. It is particularly useful when the data are qualitative, when there are insufficient data points, when the data lack precision, or when they originate from uncertain sources. Fuzzy concepts are ideal for finding optimal solutions in such cases [52]. Moreover, fuzzy methods are commonly used to quantify approximation, experimental results, and non-classical events [53]. Since emotions often influence decision-making in MCDM problems, various levels of vagueness can arise in the solutions. Therefore, it is recommended to use fuzzy concepts in these problems to better handle uncertainty and imprecision [54]. Some researchers have applied MCDM techniques in fuzzy environments to solve engineering challenges, particularly in Civil Engineering [55]. While MCDM approaches and their combinations help decision-makers rank and select the best alternatives in various contexts [56; 57; 58], few studies have explored the use of hybrid fuzzy combinations of these methods to address challenges in university education [59; 60; 61; 62]. In general, fuzzy approaches or MCDM methods have been sporadically or inefficiently applied to the quantitative and qualitative evaluation of teaching and learning outcomes in higher education. Universities and higher education institutions play a crucial role in the health and development of any society. In this study, to assess the educational status of Iranian universities, four top universities that consistently compete with each other in terms of amenities and education quality were selected: Iran University of Science and Technology (IUST), Amirkabir University of Technology (AUT), Shiraz University (SU), and Khajeh Nasir al-Din Toosi University of Technology (KUT). The study aimed to evaluate the quality of education from the perspective of master's students in Water Resource Engineering (WRE) using questionnaires. After reviewing various sources, it was concluded that MCDM techniques are rarely employed to assess educational quality in universities. In this study, the combined AHP and TOPSIS method was applied in a fuzzy environment, as this combination offers robust mathematical calculations, effectively handles weighting and normalization of initial data, and provides more reliable results. One key advantage of converting data into a fuzzy form is that it reduces the impact of random and outlier data, leading to improved results, better decision-making, and more realistic future planning. While many criteria have been proposed by researchers to evaluate educational quality, this study adopted the model proposed by [PERSON], which includes diverse, comprehensive, and measurable parameters. [PERSON]'s model consists of nine parameters: aims and objectives, content, learning activities, teacher role, materials and resources, grouping, location, time, and assessment [63; 64; 65; 66].