<p>The classical fuzzy soft set framework is extended through the incorporation of both positive and negative parametric forms to more effectively address vagueness and support multi-criteria decision-making problems. This enhancement is complemented by the formulation of well-defined operations on fuzzy soft sets, thereby facilitating efficient data extraction for practical applications. The robustness of the approach is further reinforced by the establishment of fundamental properties, such as the complement law with respect to user-selected parameters. The utility of fuzzy soft sets in decision-making is illustrated through the integration of level soft sets. The effectiveness of the proposed methodology is demonstrated via its application to a decision-making scenario, supported by a numerical example. Furthermore, the integration of the Taguchi method and Grey Relational Analysis assists in identifying critical variables and determining optimal parameter levels, thereby improving overall response performance. The validity of the results is confirmed through experimental verification using shielded metal arc welding (SMAW), which shows strong concordance with the optimal outcomes. This comprehensive approach highlights the versatility and reliability of fuzzy soft sets as a powerful tool for addressing complex, real-world problems.</p>