Soft Set theory is a strong mathematical framework that can successfully manage ambiguity and uncertainty. One of its main uses is Parameter reduction; it entails finding and removing unnecessary parameters (features or variables) from datasets without sacrificing the accuracy of the research or the process of making decisions. This method works especially well for mechanical systems, in which large, correlated datasets with multiple redundant parameters are common. The concepts of Soft Set theory and its applications for parameter reduction in several mechanical engineering areas are examined and analyzed in this study. The efficacy and efficiency of Soft Set-based techniques in lowering dimensionality and improving decision-making processes are highlighted in this research paper.