This paper evaluates the potential of fuzzy logic to improve decision-making and operational efficiency in modern transportation systems, which operate under complex, uncertain, and rapidly changing conditions where traditional control strategies often fall short. Mamdani and Sugeno fuzzy inference systems, supported by hybrid methods integrating genetic algorithms and neural networks, are applied across road traffic management, rail operations, maritime navigation, logistics, and hazardous materials transport. The proposed approach demonstrates the ability to process imprecise data, adapt dynamically to evolving environments, and strengthen system robustness. Key results include a 35% reduction in urban traffic congestion, a 22% increase in rail scheduling reliability, enhanced maritime hazard detection, and more efficient allocation of logistics resources under uncertainty. These findings underscore fuzzy logic as a flexible and scalable solution for advancing smart city initiatives, enabling autonomous vehicle technologies, and optimizing global logistics networks in next-generation transportation systems.