Explains the importance of ore grade estimation.- Reviews machine learning models for ore grade estimation.- Explains the structure of different kinds of machine learning models.- Explains different training algorithms and optimization algorithms. This chapter also explains the structure of evolutionary machine learning models.- Explains the Bayesian model averaging and multilayer perceptron networks for estimating AL2O3 grade in a mine.- Explains the structure of inclusive multiple models and optimized radial basis function neural networks for estimating Sio2 grade in a mine.- Explains the application of hybrid kriging and extreme learning machine models for estimating copper ore grade in a mine.- Explains the application of optimized group machine data handling, support vector machines, and Adaptive neuro-fuzzy interface systems for estimating iron ore grade in mines.- Presents the conclusion, general comments, and suggestions for the next books.