  The most recent concern of all people on Earth is the increase in the
concentration of greenhouse gas in the atmosphere. The concentration of these
gases has risen rapidly over the last century and if the trend continues it can
cause many adverse climatic changes. There have been ways implemented to curb
this by the government by limiting processes that emit a higher amount of CO2,
one such greenhouse gas. However, there is mounting evidence that the CO2
numbers supplied by the government do not accurately reflect the performance of
automobiles on the road. Our proposal of using artificial intelligence
techniques to improve a previously rudimentary process takes a radical tack,
but it fits the bill given the situation. To determine which algorithms and
models produce the greatest outcomes, we compared them all and explored a novel
method of ensembling them. Further, this can be used to foretell the rise in
global temperature and to ground crucial policy decisions like the adoption of
electric vehicles. To estimate emissions from vehicles, we used machine
learning, deep learning, and ensemble learning on a massive dataset.
