Machine Learning and Cloud-Enhanced Real-Time Distributed Systems for Intelligent Urban Services

Abstract

Real-time distributed systems have become fundamental to modern digital infrastructure, yet current centralized or semi-distributed frameworks still face major limitations in predictive accuracy, response latency, and resource utilization. In critical domains such as public safety and telecommunication network management, these shortcomings result in delayed decision-making, inefficient resource allocation, and vulnerability to network disruptions. To address these challenges, this paper proposes an intelligent real-time distributed architecture integrating machine learning (ML) and cloud computing (CC). By combining ML-driven predictive analytics with cloud-based elastic resource orchestration, the proposed framework enhances adaptive scheduling, dynamic fault tolerance, and real-time decision-making across heterogeneous nodes. This hybrid approach enables systems to anticipate network anomalies, optimize load distribution, and allocate resources in a risk-informed, latency-aware manner. Case studies in public safety emergency communication systems and telecommunication network optimization demonstrate how multi-source data integration, AI-assisted analytics, and cloud-edge-end collaboration can improve operational resilience, accelerate response times, and strengthen system reliability. Results indicate that the integration of ML and CC not only overcomes traditional bottlenecks but also establishes a scalable foundation for intelligent, self-adaptive distributed infrastructures. This study contributes to the advancement of resilient, data-driven urban systems and aligns with national strategic goals for digital infrastructure security, real-time disaster response, and intelligent network governance.