<p>Rising global energy demand and increasing decarbonization requirements have intensified the need for intelligent building energy management capable of handling nonlinear dynamics and multi-objective operational trade-offs. Conventional discrete-time and simulation-dependent control strategies often struggle to maintain temporal continuity, adaptive responsiveness, and consistent performance across heterogeneous building environments. Addressing these limitations, NODE-RL-BEM (Neural Ordinary Differential Equation Reinforcement Learning for Building Energy Management) introduces a unified continuous-time optimization paradigm that jointly models system dynamics and learns adaptive control policies. The approach integrates heterogeneous operational data, temporal state embeddings, neural differential equation modeling, and multi-objective reinforcement learning within a cohesive architecture designed for predictive and responsive energy optimization. Performance evaluation conducted on the ASHRAE Great Energy Predictor III dataset and the Intelligent Indoor Environment Dataset demonstrates the effectiveness of the proposed framework, achieving 42–48% energy savings, maintaining comfort violations below 0.5%, and improving indoor air quality by 28–35%. The framework further achieves a generalization score of 0.91 across diverse building operational scenarios, confirming strong transferability and stability. Continuous-time dynamics learning improves predictive fidelity and ensures smooth state evolution, while adaptive reinforcement learning enables robust decision-making under dynamic environmental and occupancy variations. Scalable applicability to multi-zone building environments highlights practical deployment feasibility. This work establishes a novel continuous-time dynamic–policy learning paradigm that integrates predictive modeling with real-time adaptive control, advancing data-driven intelligent building operation toward sustainable and autonomous energy management.</p>