The integration of machine learning with dentistry, particularly in the domain of dental materials, is progressing slowly due to a lack of widespread awareness and demonstrated applications. Our paper addresses this gap through a unified approach, structured into three core sections: data analysis, model construction, and experimental validation. In data analysis, we leverage domain knowledge to select optimal seed data and establish constraints, laying a foundation for focused exploration. The section on model construction dives into training our model within an active learning framework powered by Bayesian Optimization (BO), refining our search for optimal formulations. Finally, experimental validation tests the model's predictions against actual synthesis, using new data to iteratively enhance the model's accuracy and relevance. This concise yet comprehensive exploration aims to showcase the potential of BO and active learning in advancing dental material sciences, encouraging further exploration and adoption of these advanced methodologies in the field.