The objective of this paper is to assess the accuracy and applicability of recent state-of-art methods such as the machine learning algorithms (MLA) for mineral resource estimation. Conventional methods such as geostatistics have been predominantly used in the mining industry for this purpose with varying success. Recent advances in the use of MLA have provided a fresh approach to obtain improved accuracy in the estimation of mineral resources. Two MLA methods: the neural network (NN) and the support vector machine (SVM) have been used to estimate a polymetallic ore deposit in Alaska.The general characteristic of SVM and NN emphasises the fact that they can approximate any multivariate non-linear relation among variables. Although neural network models are able to capture the nonlinear spatial relationships that may be present in the data, they are usually difficult to optimise under sparse data settings. Of the various NN alternatives, despite their effectiveness, the model selection and estimation process is typically difficult as it involves solving complex integration and optimisation of parameters. In support vector modelling (SVM), estimating of parameters involves optimisation of a convex cost function. The working principle of SVM makes it robust against noisy and extreme value data. At the same time, it can capture the spatial distribution of ore grade more effectively with careful modelling and selection of SVM parameters.The outcome of the MLA methods were compared to those of the geostatistical ordinary kriging (OK) application to study their generalisation ability. The comparison was made using the following goodness of fit criteria: mean-squared error; mean absolute error; mean error; and coefficient of determination. The results indicated that the MLA methods may improve the predictability of ore deposits and thereby reduce the inherent risk in mineral resource estimation.