The escalating energy demand driven by global population growth and urbanization calls for a shift towards clean, renewable energy sources to mitigate greenhouse gas emissions. Hydrogen emerges as a compelling alternative fuel due to its environmental benefits and versatility. Unlike fossil fuels, hydrogen can be produced from various primary energy sources and utilized in engines or fuel cells, producing only water as a byproduct. Dimethyl ether (DME) stands out as a promising feedstock for hydrogen production, offering high energy density, low emissions, and efficient handling. Studies indicate that DME's conversion into hydrogen is more efficient compared to other sources like methanol and ethanol. The optimization of DME steam reforming and partial oxidation processes is crucial for maximizing hydrogen yield and reducing CO emissions. Advanced catalysts, such as the Cu–Zn (29–1 wt%)/Al2O3 catalyst, have shown high efficiency in DME conversion. Integrating steam reforming and partial oxidation allows for better control over the hydrogen-to-CO ratio, enhancing fuel cell performance. The combination of response surface methodology (RSM) and artificial neural networks (ANN) offers a promising approach to optimize hydrogen production processes. By fine-tuning operational parameters through advanced modeling techniques, researchers aim to achieve optimal hydrogen yields while minimizing energy consumption and environmental impact. This synergistic approach highlights the potential of DME as a sustainable feedstock for hydrogen production, driving advancements in renewable fuel technologies and catalytic processes.