This study is an exploration of a deep learning technique as it applies to jet noise prediction and modeling. A database was created describing the near-field and far-field conditions of a complex non-axisymmetric jet flow, with Mach numbers ranging from 1.0 to 1.6. The problem was posed as a form of multivariate nonlinear regression, and a Deep Neural Network (DNN) was used to create a model. Google’s TensorFlow was utilized in network construction and training, and a feature space consisting of plausible predictors of far-field directional SPL was defined, based on previous fundamental studies and jet noise scaling laws. The effects of feature space, learning rate, and network architecture modification were also studied and quantified. On average, the DNN was able to predict directional far-field SPL within ±0.75dB, surpassing our original goals. In addition, we suggest ways to extend this technique to encompass a greater range of nozzle geometries and flow conditions.