This study delves into the formalization of processes within neighborhood assemblies (NAs), a key social group structure, under the influence
of metasystems, internal capabilities, and varying decision-making conditions. The research investigates static, dynamic, and uncertain environments using functional mathematical models to understand and enhance leadership assessment mechanisms. The central focus is the application of fuzzy logic and linear regression to evaluate the leadership qualities of mahalla leaders, a critical element of local governance. By addressing the inherent uncertainty in social data, this study offers a robust framework for analyzing public opinion and identifying leadership effectiveness based on survey responses. The methodology includes data normalization, the representation of ratings through triangular fuzzy
numbers, and the construction of fuzzy regression models. These techniques allow for the comprehensive analysis of subjective and imprecise data, providing nuanced insights that traditional linear regression often overlooks. Dynamic modeling further reflects the temporal activity and behavior of NAs, enabling informed decisions on leadership and social management actions. The findings underscore the value of fuzzy logic in capturing data variability, identifying critical performance criteria, and improving community leadership practices. Moreover, dynamic models contribute to long-term predictions of social group evolution, offering practical tools for strategic decision-making