Aim: This research aims to improve the accuracy of coupon redemption prediction by contrasting Logistic Regression against K-Nearest Neighbors (KNN). Materials and Methods: The study involves two main methodologies—Logistic Regression and KNN, with dataset. A dataset comprising coupon redemption data is employed, containing a representative sample of coupons. Training and test datasets are created from this coupon dataset, with accuracy estimation performed using a training dataset of a specified size and a testing dataset. The experiment is repeated twenty times for robust analysis. Results and Discussion: The results indicated that KNN achieved a significantly higher accuracy (99%) compared to Logistic Regression (49%). The statistical analysis revealed a p-value of p = 0.001 (p < 0.05), indicating a significant difference between the two algorithms in favor of Logistic Regression. Statistical analysis utilizing the Independent Sample t-test indicates a significant distinction between the models, with a p-value of 0.001 (< 0.05) in favor of KNN. Conclusion: Logistic Regression and K-Nearest Neighbors algorithms for coupon redemption prediction were employed. The study demonstrated that KNN outperformed Logistic Regression in terms of accuracy, with a statistically significant difference between the two algorithms (p = 0.001). The findings suggest that KNN is more effective in enhancing coupon redemption prediction accuracy, emphasizing its potential for optimizing marketing strategies.