Transportation problems (TPs) are essential for optimizing logistics and supply chains. Traditional models with deterministic values for transportation costs, supply, and demand often fail to capture real-world uncertainties from fuel price fluctuations, economic instability, and perishability constraints. To address these challenges, this study introduces the Elliptic Intuitionistic Fuzzy Sets (E-IFSs) framework, which extends classical fuzzy models by capturing elliptic uncertainty in both costs and freshness requirements. We propose a Zero Point Method for TPs with E-IFSs (EIF-ZPM), combining the zero-point approach with an indexed matrix structure to improve decision-making under uncertainty. This method supports optimization in cost-sensitive, freshness-aware logistics through the flexibility of elliptic intuitionistic fuzzy numbers. A case study in perishable goods distribution illustrates the method’s practical value. A retail chain optimizes delivery of dairy, fruits, and vegetables from regional warehouses to supermarkets, minimizing waste and costs associated with short shelf lives. This work presents the first application of E-IFSs in transportation modeling, offering a flexible fuzzy optimization framework for cold chain logistics and dynamic supply environments.