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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62210
Volume:
10 |
4 |
April 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Amity Institute of information Technology
Amity university Raipur Chhattisgarh
Amity Institute of information Technology
Amity university Raipur Chhattisgarh
Amity Institute of information Technology
Amity university Raipur Chhattisgarh
Amity Institute of information Technology
Amity university Raipur Chhattisgarh
Amity Institute of information Technology
Amity university Raipur Chhattisgarh
Amity Institute of information Technology
Amity university Raipur Chhattisgarh
The increase in rapid urbanization has created complex challenges that city planners must deal with. Examples of these challenges are traffic control, resource distribution and environmental sustainability. This-research paper proposes an AI-powered predictive framework utilizing machine-learning technology and real time data to predict future trends related to urbanization and assist virtual decision-makers through improved knowledge of their city environment. For example, this system uses data related to population growth, traffic flows, and environmental conditions for analytical purposes to detect changes that could lead to problems and provides strategic alternatives to address those issues before they occur. The system also integrates with GIS technology for improved visualization, spatial planning, and enhanced accuracy in locating and defining features associated with urbanization and city planning. This study shows that the use of AI-powered predictive intelligence provides increased efficiency, lower costs, and supports sustainable urban development; therefore contributes to the development of smart and resilient cities worldwide.
KEYWORDS
According to the results of the proposed model framework supports successful predictive urban planning, but it is also highly affected by the quality and availability of the underlying data. Accuracy of prediction for examples where incomplete or inconsistent data is available will suffer. That being said, the model's performance is promising for actual use in the development of smart cities.[19]
[1] M. Batty, The New Science of Cities. Cambridge MA USA MIT Press 2013 Publisher: MIT Press 2013. [2] C. M. Bishop, Pattern Recognition and Machine Learning. New York NY USA Springer 2006 Publisher: Springer 2006. [3] T. M. Mitchell, Machine Learning. New York NY USA McGraw-Hill 1997 Publisher: McGraw-Hill 1997. [4] J. Han, M. Kamber, & J. Pei, Data Mining: Concepts and Techniques Third (3rd) Edition. Burlington MA USA Morgan Kaufmann 2011 Publisher: Morgan Kaufmann 2011. [5] Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14. [6] Zanella, A., Bui, N., A. et al. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1(1), 22-32. [7] M. Goodchild, Geographic information systems and science, International Journal of Geographic Information Science, 26(4), 571-580 2012. [8] Santana, E. F., et al., Software platforms for smart cities: concepts and challenges, IEEE Communications Magazine, 55(3), 32-39, 2017. [9] Nosratabadi et al. 2019, H., S., B., P. & Y., Z. State of the art survey of machine learning for smart cities, Sustainable Cities and Society, vol. 49, 101-110. [10] Z. Yan et al., (2023). Intelligent urbanism with artificial intelligence in smart cities. Journal of Cloud Computing, 12, 179.
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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