Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process widely adopted in multiple industries for various purposes. When LPBF is used for part fabrication, determining the manufacturability of a specific design is a challenge. Therefore, this study aimed to identify a printable design using a novel approach to predict the potential printing failures of a given design via the LPBF process. A voxel-based convolutional neural network (CNN) model is developed for analyzing the design aspect, and a neural network (NN) model is applied to the process aspect. The two models are then combined to predict the manufacturability of the given design in the selected LPBF process settings. The validation samples were selected randomly, and the results verified that the developed model can accurately predict the manufacturability of the specific design. However, the proposed model is restricted by the computational power and the number of training datasets and therefore requires further investigation in this regard.