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This research surveys the integration of Artificial Intelligence (AI) and Machine Learning (ML) to establish an autonomous framework for Digital Marketing, with a primary focus on revolutionizing traditional marketing strategies by forecasting and addressing consumer demands. The proposed approach aims to enhance decision-making processes through the application of advanced algorithms for precise demand predictions. Additionally, the study explores the efficacy of AI through ensemble machine learning, employing decision tree algorithms to optimize digital marketing indicators. The examination emphasizes the utilization of AI-powered ensemble ML to refine cost-effective strategies and maximize profits, using a dataset comprising 6561 possible tuples. Three collaborative ML approaches are working as algorithms to distinguish designs and relationships within the price data, contributing to strategic decision-making. This project uniquely illustrates the possible of replicated data to elevate cost-saving strategies in business contexts. The findings not only contribute to existing works on AI and ML requests in commercial but also highlight the transformative impact ML can have on commercial proprietors, production, and marketing personnel, extending implications to various businesses, counting transport, logistics, and trade, with significant prospects for improving overall performance of 91.75% in operational efficiency.
Singhal et al. (Fri,) studied this question.