Abstract: Integrating Artificial Intelligence (AI) and network technology represents a transformative advancement in modern networks’ protection, management, and optimisation. This literature review presents a comprehensive overview of current developments, existing challenges, and future directions for AI applications in computer networking. The primary aim is synthesising recent research to illustrate how AI-driven technologies reshape traditional network models and drive the shift toward more intelligent, autonomous, and resilient infrastructures, particularly in emerging 5G and forthcoming 6G networks. Network systems have evolved from simple analogue designs into complex digital ecosystems that support high-speed communication, intelligent devices, and data-intensive applications. However, this rapid growth has outpaced the capabilities of traditional rule-based network management approaches, highlighting the need for adaptive, real-time solutions. AI through machine learning (ML) and deep learning (DL) offers powerful data processing, pattern recognition, and autonomous decision-making capabilities, positioning it as a key enabler for managing growing complexity, enhancing security, and supporting autonomous operations. A systematic review was employed to ensure methodological rigour, focusing on peer-reviewed journal articles, leading conference papers, and expert analyses related to AI use in network security, administration, and optimisation. Thematic and comparative analyses were conducted to identify key trends, performance indicators, and innovative developments across various network layers, particularly emerging AI paradigms such as dynamic graph learning, federated learning, and explainable AI (XAI). The review finds that AI significantly improves network performance, including enhanced intrusion detection, advanced threat analysis, intelligent traffic routing, predictive maintenance, and autonomous resource allocation. Furthermore, AI is instrumental in enabling the full potential of 5G and future 6G technologies, supporting features like network slicing, ultra-low latency communication, and novel use cases such as real-time remote healthcare and immersive extended reality (XR) experiences. Despite these advancements, several research gaps remain. These include the lack of standardisation, challenges balancing model interpretability with accuracy, real-time explainability, developing lightweight AI models suited for constrained networking hardware, and concerns around privacy and ethical use. This review ultimately underscores the importance of continued interdisciplinary collaboration to ensure responsible, effective, and sustainable integration of AI into networking. As the digital landscape continues to grow, AI will be essential in driving the development of faster, more intelligent, and more secure network environments.