New research has found that AI-enhanced systems are already delivering major gains in energy forecasting accuracy, HVAC efficiency, renewable energy integration, and real-time urban infrastructure management, with some optimized systems achieving energy savings of up to 44% while maintaining thermal comfort standards. The study, titled “AI-Enhanced Strategies for Energy-Efficient Urban Environments” and published in Engineering Proceedings, maps how AI, machine learning, reinforcement learning, IoT sensor networks, edge computing, and physics-informed models are reshaping urban energy systems while identifying major technical, governance, and cybersecurity barriers that continue to limit large-scale deployment. AI-driven predictive analytics and closed-loop control systems are fundamentally altering how cities manage buildings, transportation systems, power grids, and renewable energy infrastructure. Modern urban environments now generate massive volumes of real-time data from smart meters, IoT sensors, environmental monitoring systems, occupancy tracking, mobility networks, and climate datasets, creating the foundation for advanced machine learning applications in energy optimization. Researchers explained that supervised machine learning models are already demonstrating extremely high accuracy in predicting electricity demand and building energy consumption. Random Forest Regression models achieved R² values of up to 0.9835 for electricity consumption forecasting, enabling more precise planning and energy allocation in complex urban systems. Tree-based supervised learning approaches including regression ensembles, gradient boosting, and random forests were found to achieve more than 95% forecasting accuracy for electricity demand and chiller performance. The paper also highlighted the rapid expansion of deep learning architectures in smart city energy systems. Hybrid CNN-LSTM models outperformed traditional statistical forecasting methods in predicting weekly energy demand across residential and commercial buildings, achieving prediction accuracies of up to 97% while significantly improving operational efficiency in complex urban environments. Deep reinforcement learning emerged as one of the most important technologies examined in the study. Researchers found that reinforcement learning systems integrated into digital twin environments reduced HVAC