A Review of Machine Learning Techniques for Water Quality Assessment and Prediction

It is essential to forecast and examine water condition for public health. Because pollution is increasing, we need water condition prediction (WCP) regularly. Safe water is necessary for consumption. Traditional laboratory-oriented testing approach is accurate but slow and costly. Nowadays, machine learning (ML) and deep learning (DL) models have been used to water condition prediction (WCP) and classify water quality indices (WQI). These models have also been used for monitoring water in real time using Internet of Things (IoT) infrastructure by learning complex, non-linear relationships in data. We searched for papers on Google Scholar, IEEE Xplore, ScienceDirect, and MDPI. We used the keywords "water quality prediction", "machine learning", "water quality index", and "IoT water monitoring" to search for papers. This narrative review synthesizes the literature published between 2020 and 2026. It critically reviews machine learning (ML) and deep learning (DL) models for checking water condition, calculating water quality indices, and synthesizing real-time water condition checking using IoT.