This chapter explores Simulated Annealing (SA), a metaheuristic optimization technique inspired by metallurgical annealing. The text begins with the physical analogy of metal cooling to establish intuitive understanding. Mathematical foundations follow, covering temperature scheduling and acceptance criteria with clear explanations. Practical implementation is emphasized through Python code examples for both basic and advanced SA variants. The chapter examines applications ranging from the classic Traveling Salesman Problem to modern uses in VLSI design and portfolio optimization. Advanced topics include parallel implementations and adaptive cooling schedules. Each concept connects theory with practice through concrete examples and visualization techniques. The material provides both conceptual foundations and practical tools for applying SA to complex optimization problems.