This project will develop an advanced digital twin capable of simulating bioreactor operations and cellular behaviors in real time. The digital twin will integrate historical and real-time data to model key parameters such as pH, temperature, nutrient utilization, and metabolite production. Genetic Algorithm will be deployed to autonomously optimize bioprocess protocols, exploring thousands of configurations in a fraction of the time required for traditional methods. Further, this effort allows for comprehensive integration of the digital twin with the bioreactor control systems, establishing a robust closed-loop framework for real-time adjustments. Additionally, the project will conduct pilot testing of Reinforcement Learning in a simulated environment, setting the stage for adaptive and self-learning optimization in future phases. The scope ensures the development of a fully scalable, multi-parameter control system, demonstrating transformative improvements in efficiency, adaptability, and scalability for biomanufacturing.