This survey paper provides an extensive review of the application of Large Language Models (LLMs) in network operations and management (NO&M). It outlines the transformation in NO&M driven by LLMs, emphasizing their potential to address challenges such as network design, automation, optimization, and security. The paper explores how LLMs can enhance traditional methods by automating complex tasks, improving network agility, and providing scalable and adaptive solutions to emerging network demands. Key contributions include a detailed discussion on LLM-enabled techniques that address issues like real-time adaptability, scalability, security, and intent-based management. The survey categorizes existing research into several key areas and identifies current limitations, such as integration with legacy systems, explainability, data privacy, and scalability. Moreover, it highlights future research directions, including the need for scalable architectures, energy-efficient models, enhanced security protocols, and ethical considerations in deploying AI-driven solutions. The findings are expected to drive innovation and provide valuable insights for researchers and practitioners aiming to leverage LLMs for advanced networking tasks.