Assistant Professor, Dept. of Biotechnology RKDF University Ranchi

DOI Link (Crossref) Prefix: https://doi.org/10.63431/AIJITR/3.IV.2026.64-67

AIJITR, Volume 3, Issue –IV, July - August, 2026, PP.64-67

Received on 11th July, 2026 & Accepted on 21st July, 2026, 

Published: 1st August, 2026.

The statement “Copyright © by Author” (The author retains the copyright to the published article.)

Rapid urbanization has put a great deal of strain on water supplies, resulting in serious problems such water scarcity, inadequate distribution, and ongoing leaks. Traditional water management systems struggle to meet the demands of modern cities since they are usually characterized by manual oversight and slow reactions. In this case, cities can adopt intelligent water management plans thanks to modern technologies like artificial intelligence (AI) and the internet of things (IoT), which offer efficient, scalable solutions to these problems. Using interconnected sensors to monitor water flow, usage, pressure, and quality across the distribution network is made possible by integrating IoT technology into urban water systems. Real-time data streams produced by these IoT devices provide crucial information about the functionality and state of water supply infrastructure. Compared to traditional human methods, this continuous, automated monitoring significantly accelerates the speed of data collecting. On the other hand, AI-powered analytics improve the usefulness of real-time data by using machine learning algorithms to analyze big datasets. These algorithms can predict future patterns in water demand, improve resource distribution, and detect abnormalities (like leaks). Water managers can anticipate usage spikes, possible system failures, or essential maintenance jobs by using AI models that can produce predictive insights from past data. In particular, AI-enabled predictive maintenance can prevent small issues, such as leaks or pipe clogs, from growing into costly system-wide failures. In order to address issues with urban water management, this study explores the design, uses, and advantages of AI and IoT technology. It looks at specific cases where these technologies have been used successfully to ensure high water quality, improve distribution efficiency, and reduce water waste. For example, AI and IoT assist reduce operating costs and environmental impacts while guaranteeing urban areas consistent access to clean water by facilitating the early detection of leaks and ongoing water quality monitoring. In order to demonstrate real benefits like reduced water losses, enhanced distribution, and heightened system resilience, the study also looks at case studies from places like Barcelona and Singapore that have used AI and IoT for intelligent water management. These examples highlight the revolutionary potential of AI and IoT in creating resilient and sustainable urban water systems, which are essential as cities deal with the twin problems of population growth and climate change. The study concludes with suggestions for future research directions, emphasizing the significance of continuous developments in edge computing, blockchain, and AI models to further enhance the efficiency, security, and transparency of water management systems. As cities grow, preserving the sustainability of urban water resources will depend on how well AI and IoT technologies scale.