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Hydrogen (H2) absorption percentage by porous carbon media (PCM) is important for identifying efficient H2 storage media. PCM with H2-uptakes of greater than 5 wt% are urgently required to improve the performance of H2 fuel tanks for use in fuel-cell-powered transportation vehicles. Machine-learning (ML) methods can provide effective tools for predicting PCM H2-uptakes from influential variables determined by experiments performed on a wide range of PCM. This study evaluates the PCM-H2-uptake prediction performance of four well-established ML models: generalized-regression neural network (GRNN), Least-squares-support-vector machine (LSSVM), adaptive-neuro-fuzzy-inference system (ANFIS), and extreme-learning machine (ELM). A 2072-record database, compiled from literature, comprising eleven independent variables and PCM H2-uptake (dependent variable covering a range of 0 to 8.38 wt%) was evaluated by the four ML models. Each model was trained and validated using 10-fold cross-validation. The LSSVM generates the best PCM-H2-uptake prediction performance when applied to an independent testing subset of data records, achieving a root mean squared error of just 0.2407 wt%. Feature importance sensitivity analysis identifies pressure as the most influential of the independent variable considered. Leverage analysis identified that 96.53% of the data records of the compiled database, when predicted by the LSSVM model, resided within the applicable domain with only seventy-two data records considered as suspected outliers. These results indicate that the LSSVM model developed is highly generalizable for the purpose of predicting PCM H2-uptake from the influential variables.