In the real-world context of logistics and supply chain management, decision-making within transportation systems involves multiple conflicting objectives and pervasive uncertainty. Traditional deterministic and fuzzy approaches often fall short in capturing the dual nature of hesitation and uncertainty in data. This research introduces the application of Intuitionistic Fuzzy Sets (IFS) to model and solve Multi-Objective Transportation Problems (MOTPs) more effectively. Intuitionistic fuzzy sets, by incorporating degrees of membership, non-membership, and hesitation, provide a richer framework to model ambiguity and support more informed decisions. This paper develops an intuitionistic fuzzy multi-objective optimization framework, validates it through real-world case studies, and compares its performance with conventional fuzzy models. The results highlight the IFS model’s superiority in robustness, adaptability, and real-world applicability. The study advances decision science in transportation and establishes IFS as a powerful tool in uncertain multi-objective environments.