This study presents a novel approach to laboratory optimization using digital twins and ubiquitous computing. A digital twin framework was developed to replicate the physical laboratory environment, enabling real-time monitoring, simulation, and optimization of laboratory operations. Ubiquitous computing technologies, including sensors and IoT devices, were integrated to collect data on laboratory parameters such as temperature, humidity, and equipment usage. The digital twin model was validated through experiments in a real-world laboratory setting, demonstrating significant improvements in laboratory efficiency, reduced energy consumption, and enhanced equipment utilization. The results show a 25% reduction in energy costs, 30% increase in equipment utilization, and 20% decrease in experiment turnaround times. This research contributes to the development of smart laboratories, enabling researchers to focus on high-value tasks while optimizing laboratory operations. The proposed framework can be applied to various laboratory settings, paving the way for widespread adoption of digital twin technology in research and development environments.