<p>Heating, Ventilation, and Air Conditioning systems are essential for maintaining thermal comfort, indoor air quality, and occupant health, particularly in energy-intensive environments such as indoor kitchens. However, conventional HVAC control systems often rely on fixed-gain PID controllers that struggle to handle nonlinear dynamics, time-varying disturbances, and indoor air pollution, leading to reduced comfort and increased energy consumption. To address these challenges, this study designs and simulates an energy-efficient HVAC control system based on a hybrid Genetic Algorithm, Radial Basis Function Neural Network–PID approach for regulating indoor temperature, humidity, and carbon monoxide concentration. The genetic algorithm is first employed to obtain optimal baseline PID gains, ensuring fast and stable initial system response, while the RBF neural network adapts these gains online to cope with environmental uncertainties and disturbances. The HVAC plant is modeled as a three-state dynamic system with realistic thermal, moisture, and pollution disturbances. The simulation results indicate accurate tracking of the reference setpoints across all controlled variables. The temperature regulation achieved a low RMS error of 0.26726 with a coefficient of determination of 0.75193, demonstrating good tracking performance. For relative humidity, the RMS error was calculated as 1.1548, while the corresponding R² value was 0.11706, Carbon monoxide regulation exhibited a lower RMS error of 0.014568 compared to temperature and humidity; and the associated R² value of 0.99539 confirms satisfactory goodness of fit and effective disturbance rejection despite intermittent pollution spikes. The proposed GA–RBFNN–PID control system demonstrated rapid convergence characteristics, where the controlled parameters settled precisely at their respective setpoints within approximately 1–2&#xa0;min with no overshoot and observable steady-state error, thereby ensuring robust and stable indoor kitchen climate regulation.</p>