AI-Driven Distributed Systems for Predictive Network Planning and Optimization in Telecommunications

Abstract

Telecommunications networks have become complex systems spread over large areas that need to handle a huge increase in data traffic, different service needs, and high reliability requirements. Traditional methods for planning networks, which rely on fixed rules and a lot of manual work, are no longer enough to handle the complexity and rapid changes in today's fourth-generation and fifth-generation networks. This article examines the integration of artificial intelligence with distributed systems architectures to revolutionize telecommunications network planning and optimization. The article looks at how using microservices, cloud technologies, and machine learning together helps with predicting outcomes, making smart decisions automatically, and managing infrastructure that can grow easily. By synthesizing insights from recent advances in telecom optimization, microservices security, coverage prediction algorithms, and fault-tolerant system design, this article demonstrates how intelligent distributed platforms overcome the limitations of conventional planning approaches. The article indicates that AI-driven distributed systems significantly enhance planning accuracy, accelerate deployment cycles, and support proactive infrastructure evolution at national and multi-regional scales. As telecommunications networks continue to advance toward software-defined and data-centric architectures, the methodologies and frameworks presented herein provide essential guidance for ensuring resilient connectivity and sustainable infrastructure development in an increasingly connected world.