The correspondence among energy use, carbon dioxide emissions, and growth is a matter of discussion among policymakers, economists, and researchers. It is not possible to deny that the concept of sustainable development inspires their enquiry into this arena. The primary aspiration of this work is to use machine learning techniques in the prediction of carbon dioxide emissions and growth by taking energy use as the input variable. Our findings suggest that the prediction accuracy of the relation between CO2 emission and growth can improve by using machine learning techniques. In this case, prediction using Adam optimization is better than stochastic gradient descent (SGD) in the context of carbon dioxide emissions and growth. Furthermore, the result highlights that the change from fossil fuel use to renewable energy use is a possible way to reduce carbon dioxide emissions without sacrificing economic growth. Hence, the policy has to be articulated in such way as to reduce fossil fuel use or increase energy efficiency, and at the same time, new investment has to be initiated in the renewable energy sector to promote economic growth in India.
Keywords: ANN; CO2; Energy use; Growth.