Previous research laboratories suffer from space inefficiency, disorganized asset tracking, and inefficient safety monitoring. To apply digital twins in a research laboratory, signal degradation in metallic environments and manual model updating must be addressed. Therefore, we developed an intelligent management framework for research laboratories by integrating 3D scanning with IoT sensors in a digital twin. Laser scanning and photogrammetry were used to establish a 3D spatial model with an average absolute error of 8.2 mm and a relative error of 0.28%. A heterogeneous IoT network was included to synchronize real-time data in the 3D model. The IoT network comprised Bluetooth low energy beacons, equipment status monitors, and environmental sensors. The developed framework showed an average positioning accuracy of 0.71 m and recognized equipment power and vibration states with accuracies of 98 and 95%, respectively. The framework decreased equipment search time by 86.7% and inventory processing time by 87.5%, whereas space utilization increased by 18%. By integrating high-precision spatial models with IoT sensors, laboratory operations are advanced from reactive manual routines to data-driven predictive management. The framework can be improved to overcome interference in high-density metallic zones and introduce automated spatial calibration. This necessitates the integration of autonomous simultaneous localization and mapping, predictive deep learning models for asset maintenance, and precise simulations into the digital twin.