American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/4092 2018 2018 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt; Jadara Research Center, Jadara University, Irbid 21110, Jordan Marwa Marwa School of international languages Zhengzhou University, Henan, China Asifa Iqbal School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China Shahid Mahmood Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA; Applied Science Research Center. Applied Science Private University, Amman, Jordan S. K. Towfek In today’s interconnected global economy, accurate financial forecasting is critical for strengthening corporate decision-making, mitigating investment risks, and maintaining competitive advantage over the long term. Traditional forecasting models often struggle with the complexities of high-dimensional and nonlinear financial data. To address this challenge, we present a hybrid forecasting framework that integrates advanced machine learning techniques with an intelligent optimization algorithm. Specifically, the model combines Long Short- Term Memory (LSTM) networks with the Football Optimization Algorithm (FbOA) to optimize key features and tuning parameters. This approach yields more stable, efficient, and accurate financial predictions using a compact set of influential variables. The proposed framework offers a cost-effective solution for corporate finance applications, enhancing investor confidence and supporting strategic economic development. By bridging cutting-edge AI methodologies and practical financial analytics, this study highlights the transformative potential of hybrid models in reshaping financial forecasting in dynamic markets. 2025 2025 24 41 10.54216/AJBOR.130102 https://www.americaspg.com/articleinfo/1/show/4092