The number of academic studies addressing advancements in Multi-Criteria Group Decision-Making (MCDM) has been steadily increasing in recent years, with the Analytic Hierarchy Process (AHP) emerging as the most widely utilized method. However, traditional weight determination approaches in AHP are often inadequate for capturing the complexity, nonlinearity, and irregularity of real-world scenarios. Consequently, these methods are increasingly being substituted by optimization models, especially in MCGDM, where results from multiple experts must be aggregated. This paper presents a novel Fuzzy Non-Linear Programming (FNLP) model that enables extraction of weights from diverse types of fuzzy numbers simultaneously. Unlike conventional approaches that require aggregation operators, the proposed model directly derives crisp weights from the fuzzy decision matrices of multiple experts. Particle Swarm Optimization (PSO) algorithm is used to solve the proposed FNLP model. This innovative framework offers a streamlined and more accurate solution for weight determination, enhancing decision-making effectiveness in complex and uncertain environments. To validate the applicability of this, the model is employed in a problem of factors evaluation responsible for IoTs successful employment readiness in industries. The results match with practical implications.