In this research, we present a sophisticated deep learning model developed for DOS calculations. The model incorporates essential structural information, pair distribution function, and atomic embedding representations as its input parameters. By combining these diverse inputs, the model achieves a comprehensive understanding of the electronic properties of materials. To train our model, we used the vast and comprehensive Materials Project (MP) structures dataset, which provides a diverse range of crystal structures.