  Reliable extraction of cosmological information from observed cosmic
microwave background (CMB) maps may require removal of strongly foreground
contaminated regions from the analysis. In this article, we employ an
artificial neural network (ANN) to predict the full sky CMB angular power
spectrum between intermediate to large angular scales from the partial sky
spectrum obtained from masked CMB temperature anisotropy map. We use a simple
ANN architecture with one hidden layer containing $895$ neurons. Using $1.2
\times 10^{5}$ training samples of full sky and corresponding partial sky CMB
angular power spectra at Healpix pixel resolution parameter $N_{side} = 256$,
we show that predicted spectrum by our ANN agrees well with the target spectrum
at each realization for the multipole range $2 \leq l \leq 512$. The predicted
spectra are statistically unbiased and they preserve the cosmic variance
accurately. Statistically, the differences between the mean predicted and
underlying theoretical spectra are within approximately $3\sigma$. Moreover,
the probability densities obtained from predicted angular power spectra agree
very well with those obtained from `actual' full sky CMB angular power spectra
for each multipole. Interestingly, our work shows that the significant
correlations in input cut-sky spectra, due to mode-mode coupling introduced on
the partial sky, are effectively removed since the ANN learns the hidden
pattern between the partial sky and full sky spectra preserving the entire
statistical properties. The excellent agreement of statistical properties
between the predicted and the ground-truth demonstrates the importance of using
artificial intelligence systems in cosmological analysis more widely.
