<p>The growing demand for renewable energy, coupled with the increasing need for sustainable and cost-effective solutions, presents significant challenges for the efficient management of hybrid renewable energy systems (HRES). This study proposes an integrated framework combining Modified Ensemble Empirical Mode Decomposition (MEEMD) and Functional Extreme Learning Machine (FELM) to improve solar irradiance forecasting and optimize the operation of solar-hydrogen hybrid systems. The model’s high accuracy, R² of 0.9934, is demonstrated using a real-world case study, highlighting its economic potential in reducing operational costs, enhancing energy dispatch, and improving overall system performance. The findings confirm that the proposed hybrid framework outperforms traditional forecasting models and provides a cost-effective solution for decentralized energy systems by optimizing both solar generation and energy storage. The model led to a Net Present Cost of 9,210,536 $ and a Levelized Cost of Electricity of 0.4676 $/kWh, demonstrating its competitiveness compared to traditional energy systems. While the study uses a specific geographic location for model training and testing, the proposed approach is globally transferable, with applications to any region with adequate solar resource availability. The model’s predictive accuracy, when applied to hydrogen production systems, can significantly reduce costs and enhance the sustainability of hydrogen-based energy solutions, making it competitive with traditional fossil fuels. This research introduces key interdisciplinary connections between high-precision solar forecasting and hybrid energy system optimization, revealing business opportunities for renewable energy developers, energy utilities, and industries involved in clean fuel production. The approach shows significant industrial potential and aligns with ongoing efforts to scale renewable hydrogen production as a competitive and sustainable energy solution in the global market.</p>