<p>Unmanned aerial systems equipped with thermal imaging cameras are vital for effective emergency response, especially in firefighting scenarios. These drones require high stability, rapid responsiveness, and precise positioning, all of which depend on advanced control systems. This study introduces an innovative approach using an Interval Genetic Algorithm to optimize Proportional–Integral–Derivative (PID) and <i>H</i>_<i>2</i> controllers, enhancing the performance of thermal imaging drones for emergency response and surveillance applications. A comprehensive mathematical model was developed to simulate quadcopter dynamics in both “ + ” and “<i>X</i>” configurations. The challenges of PID tuning and the limitations of <i>H</i>₂ controllers in real-world environments were addressed, resulting in improved drone stability and control under demanding conditions. The results demonstrate a significant enhancement in altitude control and motor speed stabilization, with an average increase of 20% in control precision and a 15% reduction in system response time compared to traditional control methods. These findings advance drone technology by providing more reliable and efficient tools for emergency responders. </p>