Hydrogen, as a clean energy carrier, is gaining importance globally. However, its high energy demands during the pre-cooling stage pose a challenge. A study proposes a hybrid approach integrating thermodynamic simulation, decision-making, and machine learning clustering to optimize refrigerant mixtures for enhanced efficiency. The performance analysis shows that components like nitrogen and methane have positive effects, while heavier components like butane and pentane have a negative impact. Methane, ethane, propane, helium, and carbon dioxide are identified as priority components in the pre-cooling process. The study identifies a top-performing refrigerant combination with a performance value of 3.80 and specific energy consumption of 1.48 (kWh/kgLH2). The research provides a data-driven framework for improving the hydrogen liquefaction process. Globally, hydrogen's role in renewable energy production and storage is crucial for transitioning to a sustainable, zero-carbon future. Efficient liquefaction processes enable the widespread use of hydrogen in sectors like transportation and industry, aiding decarbonization efforts. The study emphasizes the significance of hydrogen as an energy carrier and highlights the potential for a hydrogen-based economy with reduced carbon emissions.