In an era where complex optimization problems are in abundance, nature continues to inspire solutions through metaheuristic algorithms. The intricate mechanisms perfected by millions of years of evolution offer unparalleled strategies for problem-solving that traditional methods often overlook. This review paper provides a comprehensive overview of nature-inspired metaheuristic algorithms, categorizing them into primary groups: swarm intelligence, bio-inspired, and physics & chemistry-inspired. In the quest for efficient optimization solutions, swarm intelligence algorithms, inspired by the collective behavior of social organisms, have emerged as a vital subfield within nature-inspired metaheuristic algorithms. This paper categorizes swarm intelligence algorithms based on their biological inspirations—predatory, scavenging, herbivore, omnivore, mutualist, and hybrid—highlighting the operational characteristics and unique strengths of each. This classification clarifies the underlying principles of these algorithms and aids researchers and practitioners in selecting the right algorithm for specific optimization tasks. Despite their effectiveness in navigating complex, multi-modal landscapes, swarm intelligence algorithms face challenges such as early convergence and parameter sensitivity. Recent advancements have led to hybrid approaches and novel applications across diverse fields, emphasizing their adaptability and simplicity in implementation. By exploring future research directions, this paper aims to inspire innovative solutions for tackling optimization challenges in dynamic environments.