This chapter introduces the core concept of optimization and its importance in solving scientific, engineering, and industrial problems worldwide. It explores the broad categories of optimization, namely discrete and continuous, and highlights the increasing interest in nature-inspired algorithms for solving complex, real-world problems. Among these, meta-heuristic algorithms, especially the Grey Wolf Optimizer (GWO), have shown promise in areas like classification, learning, and prediction. The chapter discusses the limitations of existing algorithms, outlines the motivation for modifying GWO, and sets the foundation for developing a new ensemble model using a Modified Grey Wolf Optimizer (MGWO) for stock market prediction. It also presents the problem statement, research questions, objectives, scope, and significance of the study, alongside an overview of the book’s organization and research framework.