American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/4091 2018 2018 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Doaa Doaa Achieving the United Nations Sustainable Development Goals (SDGs) requires robust forecasting tools capable of capturing complex temporal and multi-dimensional patterns in global sustainability data. Traditional statistical models often struggle with the high dimensionality and nonlinear dynamics of such datasets, motivating the adoption of advanced Deep Learning (DL) methods combined with metaheuristic optimization techniques. This paper proposes a novel forecasting framework leveraging Gated Recurrent Units (GRUs), Long Short-Term Memory networks (LSTMs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), optimized using the Human-Inspired Metaheuristic Optimization Algorithm (iHOW) and its binary variant (biHOW) for feature selection. The key contribution lies in integrating metaheuristic-driven feature selection and hyperparameter tuning to significantly enhance predictive performance and computational efficiency in SDG forecasting. Results highlight substantial improvements over baseline models: the GRU baseline achieved an R2 of 0.8037 with a Mean Squared Error (MSE) of 0.0772; application of biHOW for feature selection improved the GRU’s performance to an R2 of 0.9251 and MSE of 0.0011; and further hyperparameter tuning with iHOW elevated performance to an R2 of 0.9671 with MSE maintained at 0.0011. These results demonstrate the effectiveness of iHOW in balancing exploration and exploitation, providing high-accuracy forecasts with reduced error, thereby supporting more informed decision-making. The implications extend beyond sustainability analytics, presenting transferable forecasting frameworks for data-driven, real-time decision support in business sectors such as finance, energy, healthcare, and climate risk management. This alignment of predictive analytics with strategic financial and operational planning underscores the commercial value of integrating AI-driven forecasting into sustainability-focused investment and policy frameworks. 2025 2025 01 23 10.54216/AJBOR.130101 https://www.americaspg.com/articleinfo/1/show/4091