Abstract
<jats:p>Ensuring the quality and potability of water has become critical due to an ever-increasing demand for safe, high-quality water in this era, where every basic need faces obstacles in the challenges of resource management and quality control from pollutants. This study presents a detailed analysis of numerous factors in water quality analysis and implements a range of machine learning systems for analysing and classifying water's potability. A review of existing literature and a comparative analysis in the field are conducted to evaluate the effectiveness of algorithms and preprocessing techniques that showcase methods with high accuracy and computational efficiency, as this is a key selection criterion due to the importance placed on this research. By examining key attributes obtained through testing, such as pH, Chloramine, and Trihalomethane levels, this study assesses their impact on water safety, aiming to identify models that can effectively distinguish between potable and non-potable water using the provided data.</jats:p>