Sustainable development depends on multiple factors, among which the urban mobility system (UMS) plays a crucial role, incorporating alternatives such as ride-sharing, autonomous vehicles, scheduling, and fare collection. Numerous studies have attempted to design multi-criteria decision-making methods suited to complex real-world problems. The credibility components of intuitionistic fuzzy credibility sets (IFCSs) offer an innovative framework for UMS by integrating computational intelligence (CI), the Internet of Things (IoT), and machine learning (ML). This article presents the novel concept of algebraic structures for IFCNs that effectively handle uncertainty in multiple-criteria decision-making (MCDM) problems. Operational laws for IFCNs such as inclusion, sum, and product, are developed to address uncertain information in the MCDM process. A robust hybrid MCDM framework, termed CRITIC-CIMAS-WASPAS, is proposed. The criterion importance assessment (CIMAS) method calculates objective weights by summing performance ratios across criteria. The CRITIC (The CRITIC method (Criteria Importance Through Intercriteria Correlation) method adjusts subjective criterion weights based on the correlation and variability of criteria. The weighted aggregated sum product assessment (WASPAS) method is extended to IFCSs to classify and rank the alternatives more accurately and efficiently. The proposed framework is applied to optimize urban mobility system (UMS), encompassing ride-sharing, autonomous vehicles, scheduling, and fare collection. Various validation tests demonstrate the reliability of this hybrid approach in determining optimal and reasonable decisions that are acceptable to experts and stakeholders.