Improving public transport quality significantly encourages users to shift from private vehicles, helping reduce traffic congestion, noise, and CO2 emissions in urban areas. Policymakers and researchers focus on identifying the key factors for enhancing public transport quality and finding practical solutions. However, traditional decision-making techniques often encounter limitations, such as difficulty in managing complex and non-linear relationships, inadequate solution space exploration, and defuzzification-caused weight distortion. To overcome these challenges, a novel Decomposed Fuzzy Set based Non-Linear (DFNL) optimization model is developed in this study. With this innovative model, Decomposed Fuzzy (DF) judgments lead straight to precise weights, eliminating information loss and improving precision. A hybrid metaheuristic algorithm combining Particle Swarm Optimization (PSO) and the Simplex Search Method (SSM) is proposed to solve the DFNL model effectively. Furthermore, a ranking technique called Multiplicative form of Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) is incorporated for evaluating the solution for Urban Transport Sustainability (UTS). The proposed assessment is tested on two illustrative examples to demonstrate improved performance. A case study conducted in Kolkata, India, further validates its applicability. Comparative evaluations highlight its advantages over existing methods, while its resilience and stability are confirmed with sensitivity evaluations. By integrating metaheuristic algorithms with advanced group decision-making methodologies, this approach ensures enhanced accuracy of weight, streamlined computational complexity, and adaptability to uncertainty. The study offers practical and actionable insights for policymakers aiming to implement sustainable and resilient urban transport strategies.