One of the fastest-growing branches of material science is the prediction of material properties using alternative methods instead of ab initio methods such as density functional theory (DFT), because one of the main problems of computational ab initio methods is that with increasing the scale and size of the system, the computational cost is rapidly increasing and requires heavy computing. One of these alternatives is machine learning, which significantly reduces these costs. One of the valuable physical properties of a potential energy surface function is a multidimensional real value function that provides the potential energy of a system as a function of atomic positions. The concept of potential energy surface is derived from the Born-Oppenheimer approximation of quantum mechanics. If the position of the atoms, the charge of the nucleus, and the total charge of a system are known, one can calculate its potential energy with the electron Hamiltonian, which has a heavy computational density functional theory. In this project, we predict the energy and HOMO [1] -LUMO [2] gap of Nano Silicon clusters through machine learning methods by using two computational packages, QMML and The RuNNer with potential Gaussian approximation method (kernel tricks) and artificial neural networks. [1] Highest Occupied Molecular Orbital [2] Lowest Unoccupied Molecular Orbital