Energy and Environment Package is a web-based “Tool-using machine learning” and “Database of materials” for the solar cell, CO oxidation, oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), CO2 reduction, battery, and sensors. The properties of the materials are calculated using density functional theory (DFT), post-DFT methods and GW approximation. The calculations are in the electronic regime considering the length and time scale. To develop the package the workflow is; first choosing of the model structure of the material and then solving the quantum mechanical based equations using codes in the supercomputer to extract the output. Afterward, post-processing tools are used on the output of DFT calculation to generate the plots and figures of different properties. All the information of the material is then projected in the energy and environment package under a particular database module. Python programming with the concept of machine learning is used to predict the best material from each category to guide the researchers and Industry in the relevant areas. The whole workflow will be automated using a single package by the year 2023. Our target is to add about two thousand materials information in each database module by the year 2023.