The study highlights the application of machine learning in predicting the enthalpy of formation for high-entropy hydrides, which play a crucial role in safe and high-density hydrogen storage for a clean-fuel economy. By employing Gaussian process regression with four different kernels, the researchers were able to train the ML models using 420 data points from literature and subsequently predict hydride formation enthalpy for the TixZr2-xCrMnFeNi system. These predictions were found to be in line with experimental data and density functional theory, showcasing the consistency and accuracy of the machine learning models. The research emphasizes the significance of high-entropy alloys in reversible hydrogen storage at room temperature and the challenges in designing these materials with appropriate thermodynamics. The study concludes by introducing machine learning as a rapid and reliable approach for the design of high-entropy alloys tailored for efficient hydrogen storage, revolutionizing the exploration and development of materials in this field.