<p>Nowadays, due to increasing carbon emissions, environmental issues are becoming more severe, posing serious threats to both human life and the ecosystem. The proposed work aims to reduce carbon emissions by incorporating carbon taxation policies. When the production process generates deteriorating items along with some defective units, inspection is required before delivery. Over time, deteriorating items may become damaged; however, the rate of deterioration can be reduced through the use of preservation technology, which also helps improve product quality. The demand for seasonal products may increase or decrease depending on weather conditions. The imprecise nature of seasonal demand affects the buyer’s total profit, which may fluctuate; however, the buyer aims to achieve sustainable profitability under trade-credit policies. This study proposes a sustainable EOQ model integrating credit policy, item preservation, environmental sustainability, inflationary effects, and statistical learning distributions for deteriorating and defective items under the influence of a monsoon fuzzy environment. Upon receiving a lot, the buyer inspects and classifies items into defective and non-defective categories. The proportion of imperfect items follows a learning curve across successive shipments. Non-defective items are used to meet demand, while defective items are sold at a reduced price in secondary markets. The buyer accounts for both emission monitoring costs due to carbon release and preservation costs associated with defective items. The primary objective is to maximize the buyer’s profit with respect to the retailer’s cycle time under a fuzzy monsoon environment. A numerical example is provided to demonstrate the validity of the proposed model. Finally, a sensitivity analysis is conducted to assess the robustness of the model, and potential limitations and future research directions are discussed.</p>