In this study, we address the critical challenge of enhancing energy efficiency in existing long-term care centers in Taiwan, aligning with the nation’s 2050 net-zero emissions goal while accommodating an aging population. We present a novel framework integrating the Building Energy Simulation and AnalysIs platform (BESTAI), a user-friendly building performance simulation tool, with JMP software for experimental design and statistical analysis. The methodology was applied to a case study of a typical Tier C long-term care facility in southern Taiwan. BESTAI demonstrated high accuracy with a less than 3% deviation from metered energy consumption. Four key factors were investigated: air conditioning efficiency, window-to-wall ratio, window U-value, and shading coefficient. Comparative analysis of full factorial, Taguchi, definitive screening design (DSD), and custom design experimental methods revealed that air conditioning efficiency and window shading coefficient were the most significant factors influencing building energy consumption. The DSD and Taguchi methods proved the most cost-effective, requiring only 11 and 9 trials, respectively, to achieve optimal solutions, compared with 81 trials for full factorial design. Regression models consistently identified the air conditioning system’s coefficient of performance as the most influential factor. The optimized configuration achieved a 47% reduction in electricity usage. This research provides a replicable model for rapid, accurate building energy analysis and optimization, which is crucial for Taiwan’s sustainable development in the face of climate change and demographic shifts. The findings offer valuable insights for policymakers and building managers in prioritizing energy efficiency measures in the long-term care sector. Future research can enhance this study’s optimization framework through sensor technology integration. Smart sensors can validate BESTAI simulation results in real time, with temperature and humidity sensors verifying heating, ventilation and air conditioning (HVAC) performance, occupancy sensors capturing usage patterns for demand-based control, and IoT power meters providing equipment-level consumption data. Thermographic imaging systems can monitor building envelope performance. This sensor-integrated approach can create a feedback loop between simulation predictions and actual performance metrics, enabling the continuous optimization of energy-saving strategies in long-term care facilities.