AI-Driven Predictive Analytics Framework for Real-Time Decision Support Systems
Keywords:
Real-time decision support, Predictive analytics, Artificial intelligence, Machine learning, Streaming data, Deep learning, Real-time architecture, Edge computing, Model optimizationAbstract
A real-time decision-making process has turned out to be a pressing need of contemporary sectors in the healthcare, finance, manufacturing, and smarter cities. The current paper introduces an AI-based predictive analytics model that will be used to process large amounts of data in real-time, produce insights that can be acted upon, and help make immediate decisions. The framework unites machine learning, deep learning, streaming architectures, feature engineering pipeline, and model optimization plans to increase the accuracy of prediction, response time, and reliability. The paper examines the current literature and different research gaps and offers a scalable multi-layer architecture based on data ingestion, real-time analytics, and adaptive learning. The experimental tests show that the framework is able to generate the result with high accuracy at the same time has low latency when the workload is adjusted.
Its practical constraints are reliance on high quality continuous streams of data, computationally expensive hardware requirements and model drift due to changes in dynamic environments. The future trends are to combine self-learning hybrid AI models, create lightweight edge minimized models, and increase the interpretability to address high-risk areas of application.
References
1. S. Mahamad, Y. H. Chin, N. I. N. Zulmuksah, M. M. Haque, M. Shaheen, and K. Nisar, “Technical Review: Architecting an AI-Driven Decision Support System for enhanced online learning and assessment,” Future Internet, vol. 17, no. 9, p. 383, Aug. 2025, doi: 10.3390/fi17090383.
2. H. Khude and P. Shende, “AI-driven clinical decision support systems: Revolutionizing medication selection and personalized drug therapy,” Advances in Integrative Medicine, vol. 12, no. 4, p. 100529, Jun. 2025, doi: 10.1016/j.aimed.2025.100529.
3. B. Almadani, H. Kaisar, I. R. Thoker, and F. Aliyu, “A Systematic survey of distributed decision support systems in healthcare,” Systems, vol. 13, no. 3, p. 157, Feb. 2025, doi: 10.3390/systems13030157.
4. D. Lartey and K. M. Y. Law, “Artificial intelligence adoption in urban planning governance: A systematic review of advancements in decision-making, and policy making,” Landscape and Urban Planning, vol. 258, p. 105337, Feb. 2025, doi: 10.1016/j.landurbplan.2025.105337.
5. T.-T.-T. Do, Q.-T. Huynh, K. Kim, and V.-Q. Nguyen, “A survey on video Big Data Analytics: architecture, Technologies, and open Research challenges,” Applied Sciences, vol. 15, no. 14, p. 8089, Jul. 2025, doi: 10.3390/app15148089.
6. M. Shamsuddoha, E. A. Khan, M. M. H. Chowdhury, and T. Nasir, “Revolutionizing supply chains: unleashing the power of AI-Driven intelligent automation and Real-Time information flow,” Information, vol. 16, no. 1, p. 26, Jan. 2025, doi: 10.3390/info16010026.
7. Z. N. Jawad and V. B. János, “‘A comprehensive review of AI-enhanced decision making: An empirical analysis for optimizing medication market business,’” Machine Learning With Applications, vol. 20, p. 100676, May 2025, doi: 10.1016/j.mlwa.2025.100676.
8. Prasetya, M. Wasesa, and Y. Sunitiyoso, “How can business analytics enhance Decision-Making in oil and gas surface facilities?,” IEEE Access, vol. 13, pp. 113291–113304, Jan. 2025, doi: 10.1109/access.2025.3582939.
9. Q. Abbas, W. Jeong, and S. W. Lee, “Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges,” Healthcare, vol. 13, no. 17, p. 2154, Aug. 2025, doi: 10.3390/healthcare13172154.
10. P. J. I. Chandran, S. H. A. Khalil, P. Hashir, and N. Veerasingam, “Smart technologies in aquaculture: An integrated IoT, AI, and blockchain framework for sustainable growth,” Aquacultural Engineering, vol. 111, p. 102584, Jun. 2025, doi: 10.1016/j.aquaeng.2025.102584.
11. G. Lyu, “Data-driven decision making in patient management: a systematic review,” BMC Medical Informatics and Decision Making, vol. 25, no. 1, p. 239, Jul. 2025, doi: 10.1186/s12911-025-03072-x.
12. T. S. Pillay, A. Khan, and S. Yenice, “Artificial intelligence (AI) in point-of-care testing,” Clinica Chimica Acta, vol. 574, p. 120341, May 2025, doi: 10.1016/j.cca.2025.120341.
13. M. Najafzadeh and A. Yeganeh, “AI-Driven Digital twins in Industrialized Offsite Construction: A Systematic review,” Buildings, vol. 15, no. 17, p. 2997, Aug. 2025, doi: 10.3390/buildings15172997.
14. M. Căvescu and N. Popescu, “Predictive Analytics in Human Resources Management: Evaluating AIHR’s role in talent Retention,” AppliedMath, vol. 5, no. 3, p. 99, Aug. 2025, doi: 10.3390/appliedmath5030099.
15. J. Violos, G. Mamanis, I. Kompatsiaris, and S. Papadopoulos, “Cognition and context-aware decision-making systems for a sustainable planet: a survey on recent advancements, applications and open challenges,” Discover Sustainability, vol. 6, no. 1, Apr. 2025, doi: 10.1007/s43621-025-00954-y.
16. R. Shan, X. Jia, X. Su, Q. Xu, H. Ning, and J. Zhang, “AI-Driven Multi-Objective Optimization and Decision-Making for Urban Building Energy Retrofit: Advances, challenges, and Systematic review,” Applied Sciences, vol. 15, no. 16, p. 8944, Aug. 2025, doi: 10.3390/app15168944
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