Urbanisation has significantly changed people’s living standards recently, especially regarding mobility and transportation. As cities develop and expand, reliable transit has become necessary, increasing car ownership. Although increased private and public transportation provides convenience and flexibility to city dwellers, it also adds to substantial traffic congestion and environmental pollution. Therefore, more sophisticated traffic management systems are required to manage high traffic density, optimised signal timing and frequent congestion during peak times. Intelligent transportation systems include diverse technologies to enhance urban transportation networks’ safety, sustainability and quality. Adaptive traffic signal systems play a crucial role in the framework of intelligent transportation systems. Adaptability to changing traffic conditions such as traffic volume, waiting time, delay, congestion level and pedestrians’ movement can result in more efficient traffic management for the urban network. Much work has been done on adaptive traffic signal management. Fuzzy controllers are distinguished among the existing adaptive systems due to their exceptional ability to handle uncertainty, facilitate intuitive rule-based decision-making, and excel in human-like reasoning and adaptability, which is crucial in managing the complexities of urban traffic networks. This study explores designing adaptive rule-based fuzzy controllers for isolated traffic intersections and a city-wide road network, integrating dynamic controllers with smart city infrastructure.