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Understanding voter behavior and improving election prediction are vital for strengthening democratic processes and fostering informed political engagement. Advances in artificial intelligence, machine learning, and sophisticated polling methodologies enable more accurate insights into electoral dynamics across diverse political systems. These tools not only help forecast outcomes but also reveal patterns of participation, public sentiment, and policy influence. By integrating data-driven analysis with comparative political research, society gains the ability to enhance transparency, refine campaign strategies and better anticipate the needs and concerns of the electorate. Understanding Voter Behavior With Predictive Modeling unites cutting-edge research from across the globe to illuminate contemporary trends in electoral analysis. It showcases innovative approaches that leverage predictive models, artificial intelligence, and polling methodologies to advance the study of electoral behavior. Covering topics such as big data, party loyalty, and populist campaigns, this book is an excellent resource for academicians, data scientists, policy analysts, electoral consultants, political marketing experts, and more.