Fuzzy soft sets is an extension of traditional soft sets that incorporates fuzzy logic, which provides a robust framework to handle imprecision and uncertainty in decision-making systems. Within the framework, several algorithms have been developed to handle parameter reduction and decision-making challenges, with each providing unique methodologies and applications. Despite this prominence, systematic review which covers the different aspects of studies on parameter reduction and decision-making algorithms is largely lacking. Hence, this study presents a comprehensive analysis of existing algorithms for parameter reduction and decision-making in fuzzy soft sets. The paper classifies, discuss, and evaluates key methods in terms of their computational efficiency, applicability, and effectiveness in decision support. Our findings underscore the strengths and limitations of current approaches and suggest avenues for future research aimed at enhancing the efficiency and applicability of fuzzy soft set algorithms in complex decision-making scenarios.