aspg-38-4276-1792226089 20261017083449 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Sustainable Development and Green Technology JSDGT 2836-5399 2025 5 2 Asifa Iqbal School of international languages Zhengzhou University, Henan, China Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt; Jadara Research Center, Jadara University, Irbid 21110, Jordan Accurate forecasting of gold prices remains a critical challenge in financial markets due to the nonlinear, nonstationary, and regime-dependent nature of commodity price dynamics, particularly for gold quoted against the US dollar (XAU/USD), which plays a central role as a safe-haven asset, inflation hedge, and portfolio diversifier. Motivated by the growing limitations of traditional econometric and manually tuned machine learning approaches in handling long-horizon, multi-timeframe financial data, this study proposes a robust forecasting framework that integrates deep learning with metaheuristic optimization. The main contribution of this work lies in the systematic combination of a Deep Pyramid Recurrent Neural Network (DPRNN) with advanced metaheuristic algorithms for automated hyperparameter optimization, with particular emphasis on Greylag Goose Optimization (GGO), alongside other state-of-the-art optimizers. Using historical XAU/USD data spanning from 2004 to February 2025 across multiple temporal resolutions, baseline model evaluation demonstrates that DPRNN outperforms other deep learning architectures prior to optimization, achieving a Mean Squared Error (MSE) of 0.0589, Root Mean Squared Error (RMSE) of 0.2426, and coefficient of determination (R2) of 0.79. Following optimization, the proposed GGO-optimized DPRNN framework yields a substantial performance enhancement, reducing the MSE to 2.05 × 10−5 and RMSE to 4.52 × 10−3, while simultaneously increasing the correlation coefficient to 0.987 and R2 to 0.983, with near-perfect agreement metrics reflected by a Nash–Sutcliffe Efficiency of 0.986 and Willmott Index of 0.988. These results confirm the effectiveness of GGO in navigating complex hyperparameter search spaces and significantly improving predictive accuracy and stability. From an economic and financial perspective, the findings underscore the practical value of metaheuristic-optimized deep learning models for enhancing gold price forecasting, supporting more informed investment decisions, improved risk management, and greater market efficiency in volatile and uncertain financial environments. 2025 55 73 4276 https://creativecommons.org/licenses/by/4.0/ 10.54216/JSDGT.050204 https://americaspg.com/journal/38/article/4276 https://americaspg.com/storage/articles/manuscripts/01M0KMZMC8ARQ8V8D4XSNA4YTH.pdf