While machine learning methods learn from data to make predictions or decisions without being explicitly programmed, geomechanics involves theoretical and applied sciences to understand rock and soil in response to its physical environment. Conventional rock/soil engineering faces challenges with empirical and numerical modeling solutions. However, both practices require experience and knowledge in practice. This paper aims to connect both fields; machine learning and geomechanics. It reviews standard machine learning methods to understand how they could be used to understand rock and soil mechanics. Present three examples of predictive models applied in geomechanics as well. Furthermore, it proposes de discussion about the uses of machine learning tools in the field.

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