Title: Computational Optimization of Smart Building Resource Management via Multi-Objective Reinforcement Learning and Digital Twin Integration Authors: Sh.Zh. Shinassylov, T.S. Imankulov, B.S. Daribayev, E.S. Nurakhov Abstract: Modern urban infrastructure faces the dual challenge of maximizing resource efficiency while ensuring occupant well-being, as buildings currently account for approximately 40% of global energy consumption. Traditional building automation systems often struggle with the "performance gap" between theoretical design and operational reality. This research presents a robust computational framework for an intelligent building management system (IBMS) that synergizes Building Information Modeling (BIM), Internet of Things (IoT) networks, and high-fidelity Digital Twins to bridge this gap. The methodology employs BIM-based strategic sensor placement, optimized via integer linear programming to ensure comprehensive environmental data acquisition. These data feeds are integrated into a Digital Twin environment, which serves as a high-fidelity virtual replica for simulating complex operational scenarios. The optimization core is driven by a Deep Reinforcement Learning (DRL) agent utilizing a Multi-Objective Reward Function, specifically designed to navigate the Pareto optimal trade-off between energy conservation and occupant comfort standards. A distinctive feature of this work is the practical validation through an established intelligent laboratory environment. This hardware infrastructure—comprising distributed IoT networks, Edge TPU accelerators, and ESP32-based control units—enables real-time data ingestion and model verification. By implementing transfer learning on edge computing devices, cloud-trained models are adapted to specific architectural layouts and occupant behavioral patterns. Experimental results indicate that this integrated computational approach provides a scalable solution for self-optimizing building environments and sustainable urban development.