Key Points Koopmans functionals extend DFT to predict materials' spectral properties. Calculating accurate screening parameters is computationally intensive. A simple machine learning model significantly reduces calculation time. Ridge regression was used effectively with minimal data. The model was tested on liquid water and halide perovskite CsSnI3. Future work will focus on applying this method to explore material properties further. Advancements in computational materials science often depend on determining key parameters that capture a material's physics. While these parameters can be...