Urban traffic congestion poses a significant challenge to modern cities, leading to increased travel times, fuel consumption, and environmental pollution. To address this issue and improve urban mobility, this research paper explores the utilization of fuzzy logic-based traffic signal control mechanisms. In order to optimize traffic light regulation, this study uses the Analytic Hierarchy Process (AHP) to systematically find the weights of different criteria. The research creates a solid basis for determining the most important criteria by using fuzzy matrices and fuzzy pairwise contrasts. The optimized traffic light signals are built around the resultant weighted criteria, which help prioritize variables that are crucial for improving urban traffic flow and efficiency. Because of its flexibility and capacity to learn from experience, fuzzy logic shows promise as a tool for dynamically adjusting traffic light timings. This study explores the fundamentals of traffic light control using fuzzy logic, including linguistic variables, fuzzy rule bases, rule aggregation, and defuzzification. It demonstrates the flexibility of fuzzy logic in dealing with variables like traffic volume, wait times, and even the weather.