Feature selection plays a crucial role in machine learning by enhancing model interpretability, reducing computational complexity, and improving generalization. While it is well studied in supervised learning, applying feature selection in unsupervised learning remains challenging due to the absence of labeled data. Nature-inspired algorithms have emerged as powerful optimization techniques for addressing this challenge, offering effective and adaptive search mechanisms. This chapter provides a comprehensive review of unsupervised feature selection methods that leverage nature-inspired algorithms, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Bat Algorithm, Gray Wolf Optimizer, Crow Search Algorithm, and Whale Optimization Algorithm. We explore how these algorithms are adapted to unsupervised settings, their strengths and limitations, and their comparative performance based on existing literature. This review aims to offer insights into the effectiveness of nature-inspired methods in unsupervised feature selection and to highlight potential directions for future research.