Various pollutants have endangered water quality over the past few years. As a result, predicting and modelling water quality has become crucial for reducing water pollution. Traditional techniques to evaluate water quality need a lot of expertise and time. Automated analysis using Artificial Intelligence (AI) models can effectively tackle increasingly complex issues and provide more accurate results. For this purpose, recent works use several AI algorithms to predict water quality. This study intends to assess the effectiveness of different machine learning (ML) models in estimating groundwater quality in Vietnam, including Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC). Firstly, the dataset was searched for and collected from the Ministry of Environment and Natural Resources, Vietnam. Data preprocessing is performed using different techniques to convert the data into meaningful form. K- Means, a clustering method, is applied to cluster the data into distinct groups based on which water quality needs to be evaluated. Afterwards, prediction is performed to classify the data into different categories by employing various AI models. Accuracy was used to assess how well the ML models performed in prediction. The results showed that all the models used performed well in predicting water quality, but GBC performed best and most accurately. Therefore, the results suggest that the AI models’ efficacy in water quality prediction with a high level of accuracy will further improve water quality management in Vietnam and other countries.