<p>Accurately quantifying energy savings in retrofitted and operational buildings remains challenging due to dynamic occupancy, weather variability, and changing operational conditions. This study proposes an AI-enabled Energy Conservation Calculation (ECC) framework to establish dynamic energy baselines and verify energy and carbon savings under real-world operation in Singapore hotel. High-resolution operational data are integrated with a hybrid LSTM–XGBoost architecture to capture temporal patterns and nonlinear interactions in building energy use, while ECC logic translates model outputs into auditable and policy-compliant savings metrics aligned with the Green Mark certification system. The framework was deployed across multiple commercial, residential, and mixed-use buildings over a three-year period. Results show consistently strong predictive performance, with root mean square error generally below 5% across heterogeneous building types. Verified outcomes indicate cumulative emissions reductions of 3221 tCO₂e and energy use intensity improvements exceeding 60% in selected retrofitted cases. Beyond performance evaluation, the framework supports closed-loop operational optimization and produces audit-ready outputs suitable for certification and sustainability-linked finance. These results show that combining dynamic AI-based baselining with standardized energy accounting enables reliable verification of decarbonization outcomes in operational buildings.</p>