Vitrinite reflectance (Ro) analysis is a maturity indication parameter for oil or gas prone source rocks in evaluating hydrocarbon potentials. As a result of challenges in Ro calculations from pyrolysis results, finding a way to estimate the maturity of source rocks has been an interesting subject for researchers. There is a current need to improve the Ro calculated from the temperature at which the maximum rate of hydrocarbon generation occurs (Tmax) during maturation of shale formations. As this will go a long way to accelerate decision making and help avoid excessive expenditure costs on maturity determination using measured Ro for source rock samples while saving time. After the application of the conventional multiple linear regression analysis, the present study employed machine learning methods of random forest (RF), decision tree (DT), gradient boosting machine (GBM), ensembles and bagger (EnB), and multivariate adaptive regression splines (MARS) models for improving the calculated Ro of matured black shales. Total organic carbon (TOC), oxygen index (OI), the amount of carbon dioxide produced during pyrolysis of kerogen (S3), and vitrinite reflectance measurement (Ro) were used as inputs to estimate the error margin between the calculated vitrinite reflectance and measured vitrinite reflectance. The predictions from the models were then summed with the calculated vitrinite reflectance to produce an improved vitrinite reflectance measurement. The model that generated the most improved vitrinite reflectance measurement, thus, having the least amount of statistical error was selected. EnB achieved the highest accuracy with a correlation coefficient (R) of 0.82 and coefficient of determination (R2) of 0.67 for the improved Ro model. Therefore, a conclusion can be drawn from the results that EnB can adequately improve the Ro of matured rocks through error correction. © 2022