We first examine the transferability of the trained ANN potential, which was exclusively trained on a dataset of C-H clusters. We apply this trained potential to C-H systems across various dimensions: 0D, 1D, 2D, and 3D. This comprehensive analysis elucidates the accuracy and transferability of our ANN potential in predicting the energetics and structural characteristics of diverse systems. Notably, our test cases here encompass pure C systems as well as the structures where the number of H atoms exceeds the number of C atoms, a distinctive inclusion given that the training dataset lacked pure C configurations and those with C/H ≤ 1. These intentional inclusions allow us to assess the extrapolative capability of the ANN potential in scenarios absent from its original training data.