In the realm of network management and optimization, Artificial Intelligence (AI) has emerged as a transformative force, offering unprecedented capabilities to enhance infrastructure performance. This paper explores the integration of AI technologies within network systems, a practice increasingly referred to as "Intelligent Networks." By leveraging AI algorithms and machine learning techniques, Intelligent Networks can automatically analyze traffic data, predict network loads, and optimize resource allocation, thus significantly enhancing the efficiency and reliability of network infrastructure. The study begins by outlining the fundamental components of AI-driven network systems, including data collection methodologies, machine learning models, and AI algorithms specifically designed for network analysis and optimization. It delves into various case studies where AI has been successfully implemented to manage complex network tasks—such as dynamic routing, load balancing, and anomaly detection—highlighting the substantial improvements in performance and user satisfaction. Furthermore, the paper discusses the challenges and considerations inherent in adopting AI solutions, such as the need for significant training data, the implications of model bias, and the management of privacy concerns. It also explores potential future developments in Intelligent Networks, including the integration of more advanced neural network models and the expansion of AI applications in network security and IoT infrastructures.