Sound source localization in 1-Dimensional (1D) and 2-Dimensional (2D) is one of the most familiar problems in signal processing. Various types of microphone arrays and their geometry have been explored to find an optimal solution to this problem. The problem becomes more challenging for a reverberate and noisy environment. Localization of the source both in the azimuth and elevation increases the complexity further. In this paper, a convolutional neural network (CNN) based learning approach has been proposed to estimate the primary source in 2D space. Further, a noble co-prime circular conformal microphone array (C3MA) geometry has been developed for sound acquisition. The generalized cross-correlation with phase transform (GCC-PHAT)features have been extracted from the C3MA recordings, which are the input features for training purposes. The experimental results show that the learning-based estimation is more robust compared to the conventional signal processing approach. The learning-based approach also explores the GCC-PHAT features and can be adapted in an adverse acoustic environment. The performance of the proposed algorithm shows significant improvement in the root mean squared error (RMSE) and mean absolute error (MAE) scores compared to the available state-of-art methods.