Conversion of electron density of a molecular system to its ground state energy has traditionally been carried out by using quantum chemical programs utilizing DFT functionals. In the present study, we attempt a different approach for such a purpose: we utilize neural networks for mapping the electron density of a molecule to its ground state energy using the promolecule model. Our approach consists of a utilization of 2D convolutional neural network (CNN) with projected images. The electron density is obtained via promolecule density that is then projected to images along X, Y and Z axis. These images are utilized by the CNN model to predict energy. In case of 2D convolutional neural network, we obtain a featureless learning: the images are directly utilized by the CNN model to predict the energies of the boron clusters. Boron nanoclusters ranging from B4 to B40 are used for the training set. The dataset of projected images is divided into training, test and validation sets (with a split percentage: 60:20:20%). The 2D-CNN model obtained shows a good R 2 value of 0.89 on the test set.