This chapter introduces the concept of meta-heuristic optimization techniques, which have gained prominence due to their effectiveness in solving complex engineering problems. The term "meta-heuristic" refers to advanced heuristic algorithms that use trial and error methods to find feasible solutions. These algorithms are often inspired by natural phenomena, such as the behavior of honeybees and ants, and are designed to address real-world issues. The chapter highlights the importance of optimization in engineering, noting that over 150 different types of optimization techniques are used in real-time application systems. It categorizes meta-heuristic optimization techniques into four main types: evolutionary-based, physics-based, swarm-based, and bio-inspired. The study aims to explore the latest advancements in bio-inspired optimization techniques, examining their unique characteristics, optimization properties, and operational paradigms. Comparative analyses with conventional benchmarks are conducted to demonstrate the superiority of these new approaches. The findings highlight the revolutionary potential of bio-inspired optimizers and provide new directions for future research.This chapter introduces the concept of meta-heuristic optimization techniques, which have gained prominence due to their effectiveness in solving complex engineering problems. The term "meta-heuristic" refers to advanced heuristic algorithms that use trial and error methods to find feasible solutions. These algorithms are often inspired by natural phenomena, such as the behavior of honeybees and ants, and are designed to address real-world issues. The chapter highlights the importance of optimization in engineering, noting that over 150 different types of optimization techniques are used in real-time application systems. It categorizes meta-heuristic optimization techniques into four main types: evolutionary-based, physics-based, swarm-based, and bio-inspired. The study aims to explore the latest advancements in bio-inspired optimization techniques, examining their unique characteristics, optimization properties, and operational paradigms. Comparative analyses with conventional benchmarks are conducted to demonstrate the superiority of these new approaches. The findings highlight the revolutionary potential of bio-inspired optimizers and provide new directions for future research.