Urban energy efficiency is a critical challenge due to increasing population density and energy demands. Integrating Internet of Things (IoT) technologies with fuzzy logic presents a promising approach to enhancing energy efficiency in urban areas. This study proposes a Fuzzy Logic-Driven Energy Efficiency model that utilizes IoT data to optimize energy consumption. The model takes four input variables: energy consumption (kWh), occupancy level (%), external temperature (°C), and time of day (hours). The output variable is the energy efficiency score, ranging from 0 to 100. Fuzzy membership functions are defined for each input variable, categorizing them into linguistic terms such as low, medium, and high. The output variable is categorized into poor, average, good, and excellent efficiency levels. The model employs a set of fuzzy rules, incorporating real-time IoT data to dynamically adjust energy management strategies. Key rules include scenarios such as low energy consumption combined with high occupancy, which results in excellent efficiency, and high energy consumption during low occupancy, which yields a poor efficiency score. The proposed system is designed to be adaptive to varying environmental conditions and human presence, thereby providing a more accurate and responsive energy management strategy. Simulation results demonstrate that the model effectively balances energy usage with real-time environmental and occupancy data, making it highly suitable for smart urban environments. This research contributes to the development of intelligent energy management systems, fostering sustainable urban living through data-driven decision-making.