This study focuses on the utilisation of AI, Big Data, and Business Analytics to improve the ways consumers interact with advertisers across multiple devices. Ever since the buying behavior of customers has gained a dynamic proportion, companies are applying smart techniques to execute customized services. Using four different approaches including Decision Tree, K-Nearest Neighbors, Support Vector Machine, and Random Forest, the study recommends preferences forecasts, audience segmentation, and targeting of a consumer database on synthetic data. From these experimental results, it was observed that Random Forest has the highest accuracy of 91.2% while scores for SVM, KNN and Decision Tree were 88.7%, 85.4% and 83.1% respectively. Thus, Random Forest had the highest accuracy (89.6%) and F1-score of 90.2% as compared to the other models. The results that emerged from this study prove that marketing with the use of AI and better analytical tools can pinpoint such subtle distinctions in customer behavior, which can lead to the appropriate and proper decisions with little to no assistance from humans. Further, the current study also looks into the related work to explicate recent breakthroughs in the subject area by asserting an emerging appreciation of the role of AI and analytics in creating customer-centric propositions. This study offers an applicable guidance for the businesses that net wants to improve and empower the client engagement while achieving the company’s strategic objectives and optimizing the workflows for future success and client retention..