Building Intelligent Networks with AI and Advanced Technologies
DOI:
https://doi.org/10.64235/a1db7c98Keywords:
Intelligent Networks Artificial Intelligence (AI) Machine Learning Advanced Network Technologies Software-Defined Networking (SDN) Network Function Virtualization (NFV) Intent-Based Networking Network Automation Network Observability Cloud and Edge Computing Autonomous Networks Self-Healing Systems Digital InfrastructureAbstract
The rapid growth of cloud computing, edge environments, Internet of Things (IoT), and data-intensive applications has significantly increased the complexity of modern networks. Traditional network design and management approaches—largely manual and rule-based—are no longer sufficient to meet demands for scalability, performance, resilience, and security. Building intelligent networks powered by artificial intelligence (AI) and advanced technologies has become essential for supporting next-generation digital infrastructure.
This article explores how AI, machine learning, and complementary technologies are enabling the development of intelligent networks capable of learning, adapting, and autonomously optimizing operations. By analyzing large volumes of real-time and historical network telemetry, AI-driven systems can predict congestion and failures, optimize traffic flows, automate configuration management, and dynamically enforce security policies. These capabilities transform networks from static connectivity layers into active, context-aware platforms that support business agility and user experience.
The paper examines key enabling technologies such as software-defined networking (SDN), network function virtualization (NFV), intent-based networking, cloud-native architectures, and edge computing. It also discusses the role of automation, orchestration, and observability platforms in providing the data pipelines and feedback loops required for effective AI-driven decision-making. Architectural considerations—including model deployment strategies, scalability, interoperability, and resilience—are analyzed in detail.
In addition, the article addresses challenges associated with building intelligent networks, including data quality, model explainability, operational trust, security risks, and the skills required to manage AI-enabled infrastructure. Through real-world use cases and emerging best practices, this paper demonstrates how intelligent networks enhance efficiency, reliability, and security while laying the foundation for self-healing, autonomous systems that can evolve alongside rapidly changing technological and business requirements.
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