This chapter presents a detailed overview of Swarm Intelligence methods, which are inspired by the collective behaviour of natural systems such as birds, ants, bees, and wolves. It explores how these algorithms work using simple agents that cooperate to solve complex problems without centralized control. The chapter highlights key swarm-based algorithms, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), and Biogeography-Based Optimization (BBO). Each algorithm is explained with its working principle, pseudocode, applications, strengths, and limitations, particularly in the context of stock market prediction. These methods show potential in addressing real-world data challenges by enabling better learning, optimization, and decision-making. The chapter concludes with a discussion on how BBO and other swarm-based algorithms can be used with neural networks for improved predictive accuracy, setting the stage for the proposed ensemble model in later chapters.