<p>An effective inventory model is crucial for balancing cost efficiency and market responsiveness in retail, manufacturing, and pharmaceutical sectors, where uncertainties in demand, pricing, and holding costs pose significant challenges. To address these uncertainties, this study introduces a novel fuzzy Economic Order Quantity (EOQ) model that integrates full advance payment, price-sensitive demand, and time-dependent holding costs. Motivated by the limitations of traditional deterministic models, which often fail to capture real-world variability, our model employs fuzzy set theory with triangular fuzzy number (TFN), trapezoidal fuzzy number (TrFN), pentagonal fuzzy number (PFN), and hexagonal fuzzy numbers (HFN) to represent uncertain parameters such as ordering, purchasing, and holding costs, as well as demand. The Graded Mean Integration Representation (GMIR) method is utilized for defuzzification, enabling precise cost optimization. The proposed model optimizes replenishment cycle time and total cost using the Lagrangian method, offering a robust framework for decision-making under uncertainty. Key findings reveal significant cost savings (12 to 40%) across all fuzzy types, with PFN yielding the most optimized results due to its broader uncertainty modelling. Sensitivity analysis highlights the critical impact of purchasing and holding cost parameters on total cost, providing actionable managerial insights for inventory control in dynamic market environments. This study advances inventory modelling by offering a flexible and practical solution for industries facing high-cost variability and uncertain demand, particularly for seasonal goods.</p>