To reduce the cost of silicon solar cells and improve their efficiency, it is crucial to identify and understand the defects limiting the electrical performance in silicon wafers. Bulk defects in semiconductors produce discrete energy levels within the bandgap and may act as recombination centers. This project investigates the viability of using machine learning for characterizing bulk defects in Silicon by using a Random Forest Regressor to extract the defect energy level and capture cross section ratios for a simulated Molybdenum defect and experimental Silicon Vacancy defect. Additionally, a dual convolutional neural network is used to classify the defect energy level in the upper or lower half bandgap.