Cloud networks form the foundation for applications that need distributed systems and require low latency and top performance. The rising implementation of SDN alongside multi-cloud networks and edge systems has created significant hurdles in managing instantaneous traffic flow patterns and security threats together with network congestion. Conventional network management using rules is unable to properly control the large, diverse security threats present in current cloud environments. The investigation demonstrates how Artificial Intelligence pursues optimization of cloud network operations by utilizing reinforcement learning (RL) and deep learning alongside graph-based models. The paper examines AI deployment within three fundamental fields - dynamic traffic engineering, Quality of Service optimization, and security-based anomaly detection. The integration of reinforcement learning agents demonstrates their ability to perform adaptive real-time network traffic routing in combination with supervised and unsupervised learning models, which produce congestion predictions for QoS policy enforcement. Network intrusion detection has been successfully enhanced through the integration of AI systems in SDN-enabled cloud environments. The application of intelligent networking for cloud service providers is demonstrated through detailed research involving Microsoft Azure and Google Cloud. The paper examines various production challenges regarding AI deployment in networks that involve stability issues and explainability demands and require robustness for adversarial inputs and cross-layer orchestration. Digital service security, high performance, and adaptability will rely on intelligent networking infrastructure as cloud systems evolve.