Abstract
Water is a fundamental resource for life, yet maintaining its quality remains a significant global
challenge due to pollution from industrial, agricultural, and domestic sources. Traditional water quality monitoring
methods are time-consuming and limited in scope. Machine Learning (ML) offers advanced capabilities for analyzing
complex datasets, predicting pollution levels, and optimizing water resource management in real-time. This paper
explores the integration of ML in water quality monitoring and management, reviewing current applications,
methodologies, and future research directions. We present a framework for real-time monitoring using ML models to
enhance decision-making and resource planning.