  The study of the cosmic Dark Ages, Cosmic Dawn, and Epoch of Reionization
(EoR) using the all-sky averaged redshifted HI 21cm signal, are some of the key
science goals of most of the ongoing or upcoming experiments, for example,
EDGES, SARAS, and the SKA. This signal can be detected by averaging over the
entire sky, using a single radio telescope, in the form of a Global signal as a
function of only redshifted HI 21cm frequencies. One of the major challenges
faced while detecting this signal is the dominating, bright foreground. The
success of such detection lies in the accuracy of the foreground removal. The
presence of instrumental gain fluctuations, chromatic primary beam, radio
frequency interference (RFI) and the Earth's ionosphere corrupts any
observation of radio signals from the Earth. Here, we propose the use of
Artificial Neural Networks (ANN) to extract the faint redshifted 21cm Global
signal buried in a sea of bright Galactic foregrounds and contaminated by
different instrumental models. The most striking advantage of using ANN is the
fact that, when the corrupted signal is fed into a trained network, we can
simultaneously extract the signal as well as foreground parameters very
accurately. Our results show that ANN can detect the Global signal with
$\gtrsim 92 \%$ accuracy even in cases of mock observations where the
instrument has some residual time-varying gain across the spectrum.
