This study develops a Neural Network (NN) model to predict carbon diffusivity (DC) in austenite under gas carburization conditions. The carburization process enhances surface hardness and wear resistance after quenching, by locally increasing the carbon content. By knowing DC, the efficiency of the carburization process can be enhanced. A fully empirical and a combined empirical-synthetic database are used for training the NN model. Synthetic data are generated using physics-based thermodynamic and mobility software, preventing data scarcity. Extensive pre-processing, including dimensionality reduction study, principal component analysis, and correlation analysis, controlling training input quality. The NN model uses supervised learning, estimating prediction and confidence intervals for DC.