Kriging frequently struggles to predict high variation and nuggety gold systems due to its linear interpolation nature of estimation. We design a neural network-based machine learning system to predict gold mineralization as a replacement to traditional domaining and kriging and apply it on an underground narrow vein gold deposit at a producing asset in the Northern Goldfields region in Western Australia. Our results show the machine learning can substantially improve mine planning and drill targeting for complex narrow vein gold deposits.