MCDM methodologies face significant hurdles in real-world application due to several factors. Handling multiple, often conflicting criteria makes finding a truly optimal solution difficult, especially with more complexity. Establishing accurate criteria weights is prone to subjectivity and can drastically alter outcomes, becoming contentious in group settings with differing expert opinions and often lacking precise quantitative information. Dealing with hybrid and uncertain data, requiring the integration of quantitative and qualitative assessments and the management of vagueness through methods like fuzzy set theory, presents another major challenge. Methodological limitations, such as overlooking criteria interdependencies and the static nature of many techniques in dynamic environments, further complicate matters. Practical concerns include the high computational demands of some methods and the lack of transparency in single aggregated scores, hindering stakeholder trust. Finally, existing models struggle with subjectivity in expert input, oversimplification through single linguistic terms, and the inability to effectively process complex, uncertain, and interdependent data, particularly in group decision-making where critical details or varying expert importance may be overlooked.