Natural Language Processing (NLP) has become essential in text classification, sentiment analysis, machine translation, and speech recognition applications. As these tasks become complex, traditional machine learning and deep learning models encounter challenges with optimization, parameter tuning, and handling large-scale, high-dimensional data. Bio-inspired algorithms, which mimic natural processes, offer robust optimization capabilities that can enhance NLP performance by improving feature selection, optimizing model parameters, and integrating adaptive learning mechanisms. This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—across core NLP tasks. We analyze their comparative advantages, discuss their integration with neural network models, and address computational and scalability limitations. Through a synthesis of existing research, this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP, offering insights into hybrid models and lightweight, resource-efficient adaptations for real-time processing. Finally, we outline future research directions that emphasize the development of scalable, effective bio-inspired methods adaptable to evolving data environments.