Emotional and psychological difficulties are common among college students and can harm both their health and their ability to study. These complicated concerns are not adequately addressed by traditional assistance systems that use single- criterion approaches. For this, the research introduces Emotional Customization Support using Fuzzy Multicriteria Decision (EMOTICS-FMD) system, which integrates multiple criteria in both quantitative and qualitative manners to provide customized emotional support for college students. The EMOTICS-FMD involves three phases, with an initial application of the Fuzzy Analytic Hierarchy Process (FAHP) to dynamically adjust the weights of the criteria based on gathered student feedback. Secondly, to rank the available support alternatives for each scenario, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) has been used. Thirdly, the Mamdani inference system is utilized to address the inherent uncertainty in emotional assessment and support provision, leading to more accurate decision-making and an engaging, effective support system for students. The model has been validated using a publicly available Kaggle dataset comprising over 102 college students from diverse academic backgrounds. The quantitative factors, such as age, year of study, and Cumulative Grade Point Average (CGPA), along with qualitative variables, including students’ self-reported emotional state and the level of difficulty of their courses, serve as input criteria. The performance results show that, compared to standard single-criterion methods, the EMOTICS-FMD system significantly enhances the accuracy and customization of emotional support recommendations.

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