Artificial Intelligence for Real-Time Predictive Analytics in Smart Systems
Abstract
Artificial Intelligence (AI) has become a foundational technology for enabling real-time predictive analytics in smart systems, including smart cities, healthcare infrastructures, industrial IoT environments, intelligent transportation networks, and energy grids. Real-time predictive analytics integrates machine learning, streaming data processing, edge computing, and distributed architectures to analyze continuous data flows and generate actionable insights with minimal latency. This paper reviews the architectures, algorithms, and system-level integrations that enable AI-driven real-time prediction in smart environments. It discusses advanced learning models such as deep neural networks, reinforcement learning, and ensemble methods for dynamic decision-making. The review further examines sensor fusion, streaming frameworks, uncertainty modeling, and scalability challenges. Ethical, legal, and security considerations—such as data privacy, fairness, robustness against adversarial attacks, and governance—are analyzed in the context of high-stakes automated systems. Finally, technical challenges including computational constraints, explainability, validation, and human oversight are explored alongside emerging research directions such as federated learning, neuromorphic computing, and edge intelligence. The study provides a comprehensive synthesis of current technologies and future pathways for deploying trustworthy, scalable, and adaptive AI-powered predictive analytics in smart systems.
References
G. Vani et al., “Advancing predictive data analytics in IoT and AI leveraging real time data for proactive operations and system resilience,” Nanotechnology Perceptions, vol. 20, pp. 568–582, 2024.
W. Villegas-Ch, J. García-Ortiz, and S. Sánchez-Viteri, “Toward intelligent monitoring in IoT: AI applications for real-time analysis and prediction,” IEEE Access, vol. 12, pp. 40368–40386, 2024.
A. T. Keleko et al., “Artificial intelligence and real-time predictive maintenance in Industry 4.0: A bibliometric analysis,” AI and Ethics, vol. 2, no. 4, pp. 553–577, 2022.
W. Chen et al., “Real-time analytics: Concepts, architectures, and ML/AI considerations,” IEEE Access, vol. 11, pp. 71634–71657, 2023.
K. Sekar et al., “Integrating machine learning and IoT for real-time predictive maintenance in industrial ecosystems: A case study analysis,” International Journal of Research in Industrial Engineering, vol. 14, no. 2, pp. 385–409, 2025.
H. Gadde, “AI-augmented database management systems for real-time data analytics,” Journal of Artificial Intelligence in Medicine, vol. 15, no. 1, pp. 616–649, 2024.
I. H. Sarker, “AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems,” SN Computer Science, vol. 3, no. 2, Art. no. 158, 2022.
D. Alahakoon et al., “Self-building artificial intelligence and machine learning to empower big data analytics in smart cities,” Information Systems Frontiers, vol. 25, no. 1, pp. 221–240, 2023.
S. K. Jagatheesaperumal et al., “Artificial intelligence of things for smart cities: Advanced solutions for enhancing transportation safety,” Computational Urban Science, vol. 4, no. 1, Art. no. 10, 2024.
I. Ahmed et al., “A blockchain-and artificial intelligence-enabled smart IoT framework for sustainable city,” International Journal of Intelligent Systems, vol. 37, no. 9, pp. 6493–6507, 2022.
P. Nama, S. Pattanayak, and H. S. Meka, “AI-driven innovations in cloud computing: Transforming scalability, resource management, and predictive analytics in distributed systems,” International Research Journal of Modernization in Engineering Technology and Science, vol. 5, no. 12, Art. no. 4165, 2023.
U. Nweje and M. Taiwo, “Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization,” International Journal of Science and Research Archive, vol. 14, no. 1, pp. 230–250, 2025.
A. Jamarani et al., “Big data and predictive analytics: A systematic review of applications,” Artificial Intelligence Review, vol. 57, no. 7, Art. no. 176, 2024.
A. Jumagaliyeva et al., “The impact of blockchain and artificial intelligence technologies in network security for e-voting,” International Journal of Electrical and Computer Engineering, vol. 14, pp. 6723–6733, 2024.
Y. Himeur et al., “AI-big data analytics for building automation and management systems: A survey, actual challenges and future perspectives,” Artificial Intelligence Review, vol. 56, no. 6, pp. 4929–5021, 2023.
S. Z. D. Babu et al., “Analysation of big data in smart healthcare,” in Artificial Intelligence on Medical Data: Proceedings of the International Symposium on Computational Methods in Medicine and Health (ISCMM 2021). Singapore: Springer Nature Singapore, 2022, pp. 243–255.
