Abstract: Greater complexity in current computer networks, introduced in response to cloud computing, Internet of Things (IoT), and 5G technologies, has made complex approaches towards managing, optimizing, and securing network systems prominent. The traditional network management techniques, rooted primarily in strict rules and human intervention, are unable to cope with the amount of data and dynamics of current networks. Therefore, the use of Artificial Intelligence (AI) in networking is becoming a game-changer. Artificial Intelligence (AI) through such technologies as machine learning (ML), deep learning (DL), reinforcement learning (RL), and natural language processing (NLP) can indeed assist in enhancing the design, management, and security of the network system. There are a number of ways by which AI can optimize the networks. Traffic patterns for smart traffic control as well as resource allocation can be forecast using machine learning algorithms. The anomaly detection capabilities of machine learning-based systems also provide real-time security attack detection, hence mitigating the impact of attack vectors such as Distributed Denial of Service (DDoS) or malware attacks. Lastly but not the least, AI is capable of offering self-healing networks that automatically detect faults and heal themselves as required without human intervention, a business of unimaginable value in enormous systems. Reinforcement learning is very beneficial for dynamic routing and load balancing through constant adjustment of network parameters to changing conditions. Other applications of AI in the networks include optimization of Quality of Service (QoS), where applications with high priority such as video streaming or gaming are assigned the bandwidth, they require to function efficiently. In addition, with edge computing and 5G networks, the work of AI is ensuring that network resources are optimally distributed to edge devices for maximum scalability and performance. However, there are some limitations in the use of AI for networking. It needs to be extensively tested with data privacy issues, interpretability of the model, and computational complexity of the AI model. The requirement for high real-time performance puts constraints in processing the network, which can be itself a limiting factor for the use of AI in some applications. Even with these challenges, the potential of AI to revolutionize networking cannot be overstated, and work on network systems with AI at its foundation will probably yield more intelligent, more autonomous, and more secure networking choices.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Natural Language Processing (NLP), Network Management, Traffic Optimization, Anomaly Detection, Self-healing Networks, Network Security, Distributed Denial of Service (DDoS), Quality of Service (QoS), Edge Computing, 5G Networks, Autonomous Networks, Network Optimization, Real-time Performance.

| DOI: 10.17148/IARJSET.2025.12649