The analysis of total organic carbon (TOC) contents is an important activity in exploring potentially hydrocarbon-generating intervals. Petroleum source rocks have, by definition, high TOC values due to the accumulation of organic matter in anoxic and low-energy deposition environments over geological time. Such petroleum generation is called conventional because it results from the increasing temperature and pressure conditions during continuous sedimentary burial. On the other hand, unconventional generation refers to reserves formed due to another heat source, for example, associated with intrusive igneous emplacement. In the last decade, there has been a growing economic interest in exploring unconventional hydrocarbon reserves because of the abundance of its occurrences. However, finding ways to assess the total organic carbon (TOC) content at lower cost and time-consuming laboratory experiments is becoming fundamental due to the importance of TOC in the analysis of the composition of these resources. This study predicts TOC using a hybrid approach, integrating machine learning models into a Grey Wolf Optimization Algorithm to adjust their parameters. The performance of four methods was compared: Elastic Net Linear Model, Extreme Learning Machine, Multivariate Adaptive Regression Splines, and Linear Support Vector Regression. The methodology was evaluated by applying core samples of the shale gas field YuDong-Nan belonging to the Sichuan Basin. The results show that the machine learning methods assisted by the evolutionary algorithm could accurately estimate TOC and be used to further exploratory geological analysis, especially those related to the prospects of unconventional oil-gas resources.