The stressful situations faced in urban water management are growing because of fast population growth, older infrastructures, and disjointed water distribution systems in intelligent cities. Water demand levels and timely identification of leakages must be accurately predicted to guarantee sustainable use of water resources and minimize loss. The conventional use of statistical techniques and rules may not respond to dynamic patterns of consumption and real-time exceptions in water networks. In an attempt to overcome these constraints, this paper presents a Reinforcement Learning-adaptive Framework to predict water demands and curb leaks in Smart Cities. The paper will propose a system that incorporates both reinforcement learning (RL) and time-series prediction models to learn dynamically consumption patterns and optimize consumption decision-making in water distribution networks. The framework utilizes agents of RL that constantly check sensor measurements, forecast the future demand of water, and detect leaks on the basis of observed inconsistencies with anticipation. The High-level deep learning models, like the Long Short-Term Memory (LSTM) networks and RL, apply optimization-level control policies to find the most efficient allocation of water and to reduce leakages. The experimental findings indicate that the suggested model attains better accuracy of prediction of 96.8%, less water waste of 28%, and responsiveness of the system in relation to the traditional machine learning and fixed model. Moreover, the framework offers a real-time, scalable, affordable, and smart city water management system. The findings show that the adaptive systems that incorporate reinforcement learning can play a major role in enhancing water efficiency, lowering operational expenses, and sustainable urban development.

Smart Cities, Water Demand Prediction, Leakage Detection, Reinforcement Learning, LSTM, IoT Sensors, Adaptive Systems, Water Resource Management, Deep Learning, Urban Sustainability