Enhancing Digital Marketing Strategies in the Food Delivery Business through AI-Driven Ensemble Machine Learning Techniques

Abstract

The digital marketing for food delivery business is the focus of this study, which investigates the use of ensemble machine learning (ML) approaches. The study's overarching goal is to pave the way for artificial intelligence (AI)-based recommendations by analyzing consumer 25 Oct 2025 (Published Online) data with the hope of discovering consumer preferences and predicting behavior. In order to Digital marketing, Food delivery trees, naïve Bayes, and k-nearest neighbor algorithms. Both the decision tree and nearest business, Machine learning, Artificial neighbor algorithms were able to obtain perfect predictions with zero error and 100% accuracy, intelligence, Accuracy. as seen in the accuracy matrix charts. On the other hand, the naïve Bayes method was able to accurately identify labels in all classes with a minimal error rate of 0.028 and a high accuracy of 97.175%. With a success rate of over 90%, the majority vote method allows models to be integrated using less than 50% of the randomized data, which minimizes customer dissatisfaction. When taken as a whole, these ML algorithms greatly improve the efficiency and efficacy of food delivery business digital marketing campaigns by cutting down on wasted time