In this paper, we explore the application of neutrosophic sets in solving the transportation problem of supplying fresh produce from farms to supermarkets, addressing the inherent uncertainty, inconsistency, and imprecision in perishable goods logistics. Traditional optimization methods often struggle with fluctuating supply, unpredictable demand, and variable transportation conditions, making the neutrosophic approach particularly valuable. By incorporating neutrosophic numbers to represent supply, demand, transportation costs, and perishability factors, we develop a more flexible and robust decision-making framework. An algorithm based on neutrosophic set theory has been proposed to minimize the transportation cost while accounting for uncertainties in transit times and spoilage risks. The effectiveness of this method is evaluated against classical optimization techniques through benchmark scenarios, demonstrating its superiority in handling complex and uncertain supply chain challenges. A comparative analysis is presented to highlight the advantages of the neutrosophic-based approach over conventional methods.