Maintaining optimal indoor temperature is crucial for ensuring comfort and energy efficiency in various environments. Traditional fan speed control systems often rely on fixed thresholds, resulting in suboptimal performance and increased energy consumption. This study explores the use of fuzzy logic control (FLC) systems to regulate fan speed based on temperature variations dynamically. Fuzzy logic rules are implemented to achieve adaptive and optimal fan speed settings by defining the universe of discourse for temperature and fan speed. The input variable for the system is the ambient temperature, classified into three fuzzy sets: cold, warm, and hot. Correspondingly, the output variable, fan speed, is categorized into three fuzzy sets: low, medium, and high. The membership functions for these fuzzy sets are defined using triangular functions to facilitate smooth transitions between states. The fuzzy logic rules are established as follows: if the temperature is cold, the fan speed should be low; if the temperature is warm, the fan speed should be medium; and if the temperature is hot, the fan speed should be high. To validate the effectiveness of the FLC system, simulations were conducted across a range of temperatures from 0 °C to 50 °C. The resulting fan speed outputs were visualized using a heatmap, which clearly demonstrated the system’s adaptive behaviour. The heatmap analysis revealed that the fan speed appropriately increases with rising temperatures, ensuring efficient cooling and energy usage. This study highlights the potential of FLC systems in providing a more responsive and energy-efficient solution for temperature regulation compared to traditional fixed-threshold systems. The adaptive nature of fuzzy logic ensures that fan speed adjustments are smooth and continuous, enhancing both comfort and energy efficiency in real-world applications.