The selection and engineering of features play a crucial role in the performance and interpretability of machine learning models, as they directly influence the model's ability to capture relevant patterns and relationships within the data. On the other hand, it has been of considerable interest to the scientific community to understand the relationship between refractive index and band gap. Various empirical formulas have been proposed in the literature to characterize this relationship. Among the most widely cited are equations 3, 4, 5, and 6. In this study, we utilize the predicted refractive indices obtained from these formulas (µ 1 ,µ 2 ,µ 3 and µ 4 ) as features for our Machine Learning Model. For clarification, it is important to note that these values do not represent the actual refractive index of any specific material; rather, these are just features derived from those empirical formulas.