For years, Delhi, India’s capital, has been struggling with a severe air quality crisis. The city’s air quality is influenced by a complex interplay of factors, including vehicular emissions, industrial pollution, agricultural residue burning and inappropriate land allotment to tree plantation. The resultant air pollution poses significant health risks, including respiratory illnesses and cardiovascular problems for its residents. So, what is the solution? The obvious solution towards a better air quality is planting more trees. This paper supports SDG goals towards sustainable and responsible development of urbanization in an age where we are confronted with increase in pollution. It consists of an expert system based on machine learning which suggests the required tree percentage to make the AIR QUALITY INDEX better or more sustainable practices based on the land availability in the particular districts of Delhi. This proposed system is trained using data collected in our research and analysis. It is not limited to predict only the tree percentage required to plant but also suggest methods other than plantation to beat pollution. Also, it will suggest ways and methods to maintain a healthy tree. This system uses Google Earth API to analyse the districts for the plantation, SWI-PROLOG for building an expert system. An interactive UI is designed using FLASK to make it easy for the user to enter the required input data.