Swarm intelligence algorithms, based on the behaviors of natural organisms, provide efficient solutions to complex optimization problems. This chapter explores the Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO), which are widely employed in engineering optimization. The chapter begins with an overview of fundamental swarm intelligence concepts, such as self-organization and emergent behavior, followed by detailed explanations of the working principles of WOA and GWO. Real-world engineering applications, such as wireless network resource allocation, power control in wireless networks, and optimization of Quality of Service (QoS) in wireless networks, are explored, showcasing how these algorithms have been successfully implemented to solve complex, real-world challenges. The chapter also examines the strengths and limitations of WOA and GWO, offering a comparative analysis to other optimization methods. Finally, the potential future applications of swarm intelligence, including bio-hybrid systems and quantum optimization, are discussed, highlighting emerging trends in the field.