Simulation-based fault injection is an approach, which is usually conducted in the design phase to evaluate the reliability of circuits. However, this approach is frequently time-consuming, especially for large designs. This paper explores the possibilities of applying machine learning (ML) as enhancement to the fault injection to reduce the runtime of the fault verification. The proposed approach separates the target gates for fault injection into two subsets. Standard simulation-based fault injection is only performed for first subset to get the result of fault propagation in the circuit, and enables machine learning process. Based on such partial classical fault injection campaign, a dataset is built to train and validate machine learning models that are able to predict the fault injection result of the remaining gates. The machine learning approach is based on a set of relevant features that could be easily extracted based on the netlist. The dataset in this study, used to validate the approach, is obtained from the ADC digital core. We have evaluated this netlist and provided ML-driven assessment whether the fault injection in some gate will propagate to primary outputs. We have evaluated several machine learning models with different loss functions. The best prediction accuracy is 95% and achieved by a 4 layer neural network with weighted Cross Entropy.