aspg-18-3843-1791423666 20261008014106 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 Salah Ayad Jassim Renewable Energy Research Center, University of Anbar, Al Anbar, 31001, Iraq Abdalrahman Fatikhan Ataalla Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Mohammed Kareem Mohammed Renewable Energy Research Center, University of Anbar, Al Anbar, 31001, Iraq More environmentally friendly standards may be implemented as work environments, lifestyles, and our conception of a fulfilling life evolve. The COVID-19 pandemic highlighted the need for adaptable systems and revealed the flaws in our routines. Because smart cities are more flexible than traditional urban areas, they are becoming more and more important. While supporting citizens is the main goal of these networked smart city components, they also unintentionally enhance urban environments. This paper uses a methodical approach to investigate smart cities, breaking down and analyzing each component to clarify their beneficial interactions. This paper provides a direction for future research through its discussion of problems, challenges, and barriers related to the urban environment that affect the development of smart cities. Real-time monitoring is made possible by connecting these devices to the internet. The spacing between lighting poles significantly influences the overall uniformity and illuminance. This paper describes the architecture of Internet of Things (IoT)-based smart public smart utility system using forecasting techniques that interconnected with the sensors using IoT stack. Sensors are made to gather the timely data from different utility applications such as lighting, CCTV cameras, water usage, wastage volume, etc. The paper demonstrates the potential synergies between IoT and artificial intelligent for supporting smart cities. We deployed three convolutional neural networks namely: AquaNet, PredWasting and LightSage for forecasting the water requirements, wasting volume and light consumption in smart cities. Results shown that PredWasting is outperformed with 99.21% of accuracy over the other models. 2025 271 278 3843 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170119 https://www.americaspg.com/journal/18/article/3843 https://www.americaspg.com/storage/11746692566.pdf