<p>Buildings are interconnected cyber-physical systems (CPS) that couple with occupancy, heating, ventilation, and air-conditioning (HVAC), distributed energy resources (DERs), and power grids. This increasing complexity poses significant challenges for scalable modeling, control, and optimization. Existing approaches either rely on physics-based models with limited scalability, data-driven methods with weak physical consistency, or simplified representations that cannot fully capture system dynamics, restricting their applicability in real-world scenarios. To address these limitations, this study presents BESTOpt, a modular physics-informed machine learning (PIML)-based runtime environment for unified modeling, control, and optimization of interconnected occupancy–building–HVAC–DER–grid systems. The framework introduces a hierarchical structure (cluster–domain–system/building–component) and a standardized state–action–disturbance–observation data typology, enabling scalable coordination and integration of heterogeneous subsystems. Three case studies demonstrate the capabilities of the proposed framework. First, BESTOpt is compared with EnergyPlus, long short-term memory (LSTM), and a reduced-order 3R2C model to evaluate computational performance, prediction accuracy, physical consistency, and scalability from 1 to 200 buildings. Second, a 30-building residential cluster simulation evaluates 12 demand-response and retrofit strategies, showing that PV–battery integration can reduce peak-hour grid import by up to 97.8% and operating cost by up to 26.6%. Third, three deep reinforcement learning (DRL) algorithms are evaluated. Among them, Soft Actor-Critic (SAC) achieves the most balanced performance, reducing operating cost by 24.8%, grid import by 33.0%, peak demand by 41.9%, and PV curtailment by 87.3% compared to a rule-based baseline. Overall, BESTOpt provides a flexible and extensible platform for grid-interactive building analysis and advanced control evaluation.</p>