Affiliations:
1College of Arts and Science, Applied Science University, P.O. Box 5055, Manama, Bahrain
2VPAA and Computer Science Department, Applied Science University, P.O. Box 5055, Manama, Bahrain
3School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan
4University College, Korea University, Seoul 02841, South Korea
In today’s digital era, businesses are increasingly adopting innovative approaches to gather valuable data for informed decision-making and maintaining competitiveness. This study examines the application of big data analytics and predictive artificial intelligence (AI) in sales forecasting, a task that remains challenging but essential for effective demand planning and resource allocation. Traditional forecasting methods often fall short in dynamic market environments, whereas advanced techniques offer greater accuracy. Using real-world data, this research employs machine learning algorithms to uncover hidden patterns and generate reliable sales predictions. A predictive model based on the XGBoost algorithm was developed and achieved a high R² score of 0.94, with cross-validation yielding a consistent mean score of 0.94 (SD = 0.02), indicating strong predictive power and stability. The findings demonstrate the effectiveness of big data and predictive AI in improving forecast accuracy and supporting data-driven business decisions. This study highlights the practical value of integrating advanced analytics into sales forecasting processes for enhanced strategic planning.
Sales forecasting, Big data, Predictive AI, Machine learning, Business analytics
https://doi.org/10.21833/ijaas.2025.08.022
Ghazal, T. M., El Kadhi, N., & Ahmad, M. (2025). Unlocking business insights with big data analytics and predictive AI: Discovering hidden patterns for accurate sales forecasting. International Journal of Advanced and Applied Sciences, 12(8), 237–245. https://doi.org/10.21833/ijaas.2025.08.022