Traffic signal management is an important concern in transportation. Traffic prediction is attained more interest in the last few years. With the rapid infrastructure revolution and the automobile industry, all countries face traffic congestion. The development of AI and the high dimensional dataset availability has attracted many scholars to develop various techniques to maximize the green time duration. Generally, traffic signal control (TSC) is utilized in real-time due to the lower establishment price. Nevertheless, these methods do not offer good function parameters, particularly during unbalanced traffic. So artificial intelligence is utilized in traffic signal control to degrade the delay period. Some drawbacks include failure to adopt traffic demand and high training costs. So, the novel Golden Eagle-based fuzzy signal controller (GEbFSC) is presented in this article to reduce the traveling time, delay time, and queue length. The presented strategy monitors and detects the traffic from the standard traffic flow dataset. The effectiveness of the presented model is validated by comparing it with other techniques, and the improvement value is evaluated. Finally, the comparative assessment validates that the developed technique performed better than other techniques.