This chapter explores Swarm Intelligence (SI), a computational paradigm drawing inspiration from nature’s collective behaviors. SI algorithms simulate local interactions among simple agents to solve complex optimization problems. The material covers fundamental principles including stigmergy, self-organization, and feedback mechanisms. Core algorithms presented include Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Bacterial Foraging Optimization, and Firefly Algorithm. Each algorithm receives thorough examination through mathematical formulations, implementation guidelines, and practical coding examples. The chapter connects theoretical foundations with applications in diverse domains including routing, scheduling, pattern recognition, and industrial optimization. Through guided exercises and visualizations, readers gain both conceptual understanding and practical implementation skills.