Extraction unit operation is the first step in traditional Chinese medicine (TCM) product manufacturing, and it is crucial in determining the quality of the produced medicine. However, due to a lack of effective multimodal monitoring and adjustment strategies, achieving high quality and efficiency remains a challenge. In this work, we proposed an artificial intelligence (AI)-based robot platform for the multi-objective optimization of the extraction process. First, a perception intelligence method for multimodal process monitoring was established to track active ingredient transfer and production changes during the extraction process. Second, a digital twin model was developed to reconstruct the field information, which interacted with real-time monitoring data. Furthermore, the model performed real-time inference to predict future production process states by using the reconstructing information. Finally, according to the predicted process states, the autonomous decision-making robot implemented multi-objective optimization, ensuring efficient process adjustments for global optimization. Experimental and industrial results demonstrated that the platform could effectively infer component transfer dynamics, monitor temperature variations, and identify boiling states, ensuring product quality while reducing energy consumption. This pharmaceutical robot could promote the integration of AI and pharmaceutical engineering, thereby accelerating the iterative development and improvement of China’s pharmaceutical industry.