Integrating natural language processing (NLP) with robotic systems is revolutionizing human–robot interaction (HRI) by making it more intuitive and effective. This review paper synthesizes and analyzes six key studies that explore methodologies for enabling robots to understand and respond to natural language commands. These studies leverage large language models (LLMs) like BERT and CLIP, integrate multi-modal data for improved contextual understanding, and focus on user-centric design to enhance usability. This paper compares various approaches, including transformers for multi-modal alignment, adversarial learning for policy generalization, and probabilistic models for real-time corrections. Key challenges identified include data diversity, real-time processing, and the need for scalable solutions. User feedback highlights a strong preference for natural language interfaces over traditional methods. The review underscores the potential of advanced NLP techniques in robotics. It provides a framework for future research, suggesting directions such as enhanced multi-modal learning, real-time adaptation, scalability, and ethical considerations. This synthesis aims to guide and inspire future innovations in natural language-based HRI.