The dramatic rise in data traffic being generated by modern telecommunications networks leverages challenges especially related to the control of congestion over distributed network topologies, as data traffics increases at the edges and core of sensor network, there is a sharp fall in the efficiency of signal and information propagation. This decrease in turn limits the ability of edge sensors to transmit data effectively to sub-control units and then on to central control systems, it contributes into dwindling network performance. This paper aims to study these important issues and find out how both network architecture and traffic load together make a signal quality decrease in congested regime. More specifically, it concentrates on how slow signal degradation between two sensor points can affect the transmission efficiency of data. The primary purpose of this research is to explore solutions that mitigate the reduction in signal propagation efficiency caused by congestion. By studying how congestion impacts the transmission of signals from peripheral sensors back to control units and to develop ways those functions can continue even under loads of high traffic. This research will utilize the most advanced AI methods including machine learning and deep learning models to discover and enhance data flow trajectories. The model mainly focusses on signal integrity and deal with improving the congestion of telecommunication networks, as shown in Fig. The methodology involves to do this is through AI-based algorithms and predictive models on the sensor networks that were seen as bottlenecks. The proposed simulation setups will mimic traffic congestion scenarios to enabling the testing of signal transmission improvement techniques that are AI-driven. Also, the study will be evaluating automated decision-making processes in network management. By leveraging AI.