Integrating fuzzy logic with data-driven techniques significantly improves energy consumption estimation accuracy in smart buildings, promoting sustainability and efficiency. This hybrid approach leverages fuzzy logic’s robust framework for modeling complex and uncertain systems, while data-driven methodologies capture dynamic variables. By considering factors such as environmental conditions and occupant behavior, this framework provides precise and adaptable energy evaluations. The proposed model achieves impressive accuracy rates of 88 and 89% using decision trees, before and after feature extraction, respectively. Notably, the model predicts energy consumption with high precision (e.g., 209.89 J). This research underscores the importance of integrating occupant comfort and behavior into energy optimization strategies, demonstrating the potential of fuzzy logic and data-driven methodologies to advance energy-efficient practices in smart buildings.