This thesis presents a cutting-edge hybrid artificial intelligence model that integrates LSTM networks with XGBoost to predict hydrogen demand, using Germany as the focal case study. This innovative approach effectively captures complex temporal dependencies and macroeconomic influences, outperforming traditional forecasting methods by modeling both short-term volatility and long-term trends. The model demonstrates outstanding predictive accuracy (R² = 0.9956; MAPE = 1.15%), enabling a detailed analysis of hydrogen demand dynamics. Its practical relevance lies in optimizing hydrogen supply chains, reducing investment risk, and informing infrastructure planning aligned with the EU’s green transition strategy. By providing a scalable, adaptable, and data-driven framework, the model supports evidence-based policymaking, fosters innovation, and contributes to the achievement of decarbonization targets. Ultimately, this research enhances strategic decision-making for industry and the public sector, advancing sustainability and operational efficiency in the rapidly evolving hydrogen economy.