Design-related flaws in software systems have increasingly drawn the attention of developers due to their significant impact on both functional and non-functional aspects of source code. To mitigate these issues, various causes behind their occurrence were explored. Different types of code smells have been found to adversely affect software quality, highlighting the necessity to identify which smell type has the most detrimental effect on the system. To tackle this issue, a hybrid Multi-Criteria Decision-Making (MCDM) framework, combining the AHP and COCOSO methods, has been employed. An integrated approach of picture fuzzy numbers with neutral and rejecting membership functions has been adapted to effectively capture uncertainties commonly encountered in real-world scenarios. Both the said methodologies are combined to conduct an analysis on real-life datasets, and the results obtained from this approach have been notably promising.