Nawab AkramK. AravindhanK. SujathaShueb Ali KhanGagan SinghR. Rajagopal2025-06-292025-06-292025-03-0697983693691049798369369128http://doi.org/10.4018/979-8-3693-6910-4.ch006https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000612806&doi=10.4018%2f979-8-3693-6910-4.ch006&partnerID=40&md5=3c56137114ab35638562972b6bd1bc18https://gnanaganga.alliance.edu.in/handle/123456789/8249The chapter reviews consumer behavior fundamentals such purchasing decision criteria, quantification, and analysis. Next, it discusses decision trees, ensemble approaches, neural networks, regression analysis, and support vector machines for customer behavior prediction. Each algorithm's technique, merits, and weaknesses are examined. We then show how these algorithms can predict purchase intent, customer turnover, life cycle value, and other consumer behaviors. This setting presents challenges when using machine learning models, including data quality, model interpretability, and the ethics of using consumer data. The industry's current and future innovations, such as AI-driven personalization and deep learning to understand client behavior, are also highlighted. This chapter explores the intersection of machine learning and consumer behavior analysis to help researchers, data scientists, and business professionals acquire more accurate and valuable consumer insights.enConsumer BehaviorMachine LearningAlgorithmsbook-chapter