A Cloud-Integrated Telecommunications Network Optimization Model for High-Performance Data Transmission Systems

The accelerating growth of data-intensive applications, distributed cloud platforms, and latency-sensitive services has necessitated the evolution of telecommunications networks toward more intelligent, adaptive, and high-performance architectures. This review examines the development of cloud-integrated optimization models that improve end-to-end data transmission efficiency across heterogeneous wired and wireless infrastructures. Emphasis is placed on the convergence of cloud computing, network function virtualization (NFV), and software-defined networking (SDN) as enablers of scalable and programmable network environments. The paper synthesizes state-of-the-art optimization strategies—including machine learning-driven traffic engineering, multi-objective routing algorithms, dynamic resource allocation techniques, and predictive QoS/QoE management frameworks—which collectively support real-time decision-making in modern telecom systems. Additionally, the study explores the role of edge-cloud orchestration, 5G/6G network slicing, and intent-based networking in enhancing bandwidth utilization, reducing transmission delays, and ensuring system resilience under fluctuating traffic loads. The review concludes by highlighting existing limitations, emerging research opportunities, and the need for integrated cloud-native optimization models capable of supporting ultra-reliable and hyper-connected network scenarios of the future.