Abstract

In 2024, the convergence of AI-driven operations (AIOps), intent-based networking (IBN), and cloud- native infrastructure gave rise to autonomous cloud networking—a paradigm where networks adapt, secure, and optimize themselves with minimal human intervention. This research explores the design and deployment of self- managing networks across hybrid and multi-cloud environments. It investigates how AI/ML models are integrated into network control planes to enable predictive fault detection, adaptive traffic engineering, and dynamic policy enforcement. Using a survey of IT professionals and cloud architects, this study captures industry readiness, adoption barriers, and expectations surrounding autonomous networking technologies. It also examines architectural challenges, including observability, configuration drift, and real-time compliance. Through statistical analysis, the paper identifies the maturity level of autonomous networking deployments and proposes an architectural blueprint to guide implementation.