Heating, Ventilating, and Air Conditioning (HVAC) are the key enablers for indoor comfort and energy utilization in most buildings today. Nevertheless, the strong nonlinearities, variant dynamic load conditions, and external disturbances make the existing classic control methods difficult to apply in practice. In this paper, the full-fledged comparative analysis of PID and Fuzzy Logic controllers for HVAC systems is reported, overcoming some drawbacks noted in the past. The framework intentionally covers a variety of dimensions of control ignoring only those aspects, which potentially play no role or are completely irrelevant, to engage and balance energy consumption, settling time, comfort, and robustness to different real operation scenarios. The experimental findings and simulations results reveal that, as compared to the classical PID controllers, the fuzzy logic controller is better in handling the non-linearity behaviour of the system, in faster time response, lower value of overshoot and better system response to real-time environmental and occupancy changes. Besides, the hybrid evaluation approach validates that there is considerable reduction in energy consumption and effective power saving optimization that directly relates to sustainability and cost-effective objectives. The work also sets up a unified benchmarking format to set the standard for the intelligent HVAC automation with adaptive learning and AI-based control systems.