Traditional approaches for seismic characterization of source rocks use simple regressions between acoustic impedance and TOC (Total Organic Carbon) to calculate volumes of this property. In this pioneering work, we generate volumes of TOC and hydrogen index (HI), applying machine learning techniques, from elastic inversion outputs. By using multiple attributes and allowing the fitting of nonlinear models to the data, machine learning techniques provide more accurate predictions of these properties, reducing uncertainties in the characterization of source rocks. The methodology proposed in this work uses machine learning methods to automate all the source rock characterization process steps: from the automated quality control of the input data; through the extrapolation of sparse laboratory measurements to continuous well logs of geochemical properties; and culminating in the 3D estimation of these properties.