Current tools for identifying new exploration targets for gold are built for geologists to manually interpret data acquired from different sources. Scaling this approach to larger projects is not a trivial task. One possibility to tackle this problem is to use data-driven predictive modeling to discover relationships in the data which can then be applied throughout a mine to more readily identify exploration targets. Here, we propose a methodology based on machine learning that takes as input data points in space describing measured geological information in a mine, correlates this with the level of gold mineralization in known places through a 3D convolutional neural network, and uses the obtained model to estimate the level of gold mineralization in every region of the mine that has available geological information. We compare the obtained model with a baseline model and show that it outperforms the baseline in all the metrics used, providing a much more accurate estimate of presence of economic gold for geologists in their investigations.