Efficient design and operation of HVAC (Heating, Ventilation, and Air Conditioning) systems are crucial for energy savings and comfort in buildings. Head loss through duct fittings plays a significant role in the overall efficiency of conditioned air distribution systems, as it can increase fan energy consumption and reduce system performance. This study focuses on the statistical modeling of head loss in duct fittings, with the goal of optimizing HVAC system design. Using data from a series of experiments and simulations, we developed predictive models that estimate head loss in various duct fittings (e.g., elbows, tees, dampers) based on factors such as flow velocity, duct size, and fitting geometry. The study employs regression analysis and machine learning techniques to analyze the relationships between these variables and the resulting head loss. Results show that the proposed statistical models provide accurate and reliable estimates of head loss, offering insights for improving HVAC system design by selecting more efficient fittings and minimizing energy losses. The findings contribute to the development of more energy-efficient and cost-effective HVAC solutions, with implications for building energy management and sustainability.