INTERNATIONAL JOURNAL OF ADVANCE RESEARCH
IN MULTIDISCIPLINARY

The emergence of cloud computing can make the network infrastructure more large-scale, heterogeneous and dynamic, in a rapid way. Conventional rule-based manual network management doesn't work for scalability, security, fault tolerance or real-time re-configurability. In this regard, AI based network automation is recognized as a disruptive paradigm that provides intelligence, self-configuration, self-healing and self-optimization of cloud networks. This paper provides an extensive study of the design, implementation, and evaluation of AI-based network automation models for constructing scalable and secure cloud infrastructure. The proposed approach leverages machine learning (ML), deep learning (DL) and reinforcement leaning (RL) in combination with SDN/NFV to support smart decision making over network provisioning, traffic engineering, anomaly detection, as well as security enforcement. With predictive analytics and real-time telemetry data, it dynamically adjusts to changes in network workloads and eases network congestion as well as being smart about security with processes that can recognize DDoS attacks, insider threats and zero-day vulnerabilities. In addition, the paper introduces a smart policy orchestration layer which maps network automation to organizational security policies and service-level agreements (SLAs). Experimental evaluation on a testbed based on the cloud clearly shows that the AI-driven automation framework significantly outperforms conventional static/rule-based network management in achieving high and efficient throughput, low latency and overheads, and accurate threat detection. The results underscore the fundamental necessity AI-based automation for resilient, scalable, and secure cloud infrastructures and elevate it as a foundational technology in next-generation cloud (and multi-cloud) environments.