Amidst an overload of misinformation, the need for precise fake news detection is of utmost importance. This study comprehensively examines cutting-edge metaheuristic techniques in fake news detection. These nature-inspired methods help in confronting the challenges posed by fake news. The research investigates metaheuristic algorithms, including Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and Physical-Based Algorithms, to boost the accuracy of fake news identification. These techniques counter misleading strategies by considering linguistic, semantic, and contextual factors. The review synthesizes recent progress in fake news identification, evaluates their significance, and skeletons a roadmap for further research in handling fake news. It emphasizes the vital role of metaheuristic methodologies in strengthening society’s defenses against misleading information in the digital age.