This article presents a systematic approach for correlating the refractive index of different material kinds and forms with experimentally measured inputs like wavelength, temperature, and concentration. The correlation is accomplished using neural network models, which can deal effectively with the nonlinear nature of the problem without requiring a predefined form of equation, while taking into account all the parameters affecting the refractive index. The proposed methodology employs the powerful radial basis function network architecture and the neural network training procedure is accomplished using an innovative algorithm, which provides results with increased prediction accuracy. The methodology is applied to two cases, involving the estimation of the refractive index of semiconductor material crystals and an ethanol-water mixture and the results show that the refractive index predictions are accurate approximately to the same number of decimal places as the real measurements. Comparisons with other neural network training methods, but also with empirical forms like the Sellmeier equation, highlight the superiority of the proposed approach.