This study focuses on predicting the Water Quality Index (WQI) in Mirpurkhas, Sindh, Pakistan, using various machine learning algorithms. The research aims to address the limitations of traditional water quality assessment methods, which are time-consuming and prone to errors. By collecting 422 data samples, the study employs Random Forest, Gradient Boosting, SVM, KNN, and Decision Trees to predict WQI. The results show that Random Forest and Gradient Boosting achieve the highest accuracy of 95% and 96%, respectively, followed by SVM at 92%, KNN at 84%, and Decision Trees at 77%. The study also conducts an uncertainty analysis using the R-factor to assess the reliability and consistency of the models. The findings highlight the effectiveness of machine learning in accurately predicting WQI and provide valuable insights for informed decision-making in groundwater management. The study concludes by emphasizing the importance of considering uncertainties in environmental assessments and suggests future research directions to enhance the predictive accuracy and understanding of water quality dynamics.This study focuses on predicting the Water Quality Index (WQI) in Mirpurkhas, Sindh, Pakistan, using various machine learning algorithms. The research aims to address the limitations of traditional water quality assessment methods, which are time-consuming and prone to errors. By collecting 422 data samples, the study employs Random Forest, Gradient Boosting, SVM, KNN, and Decision Trees to predict WQI. The results show that Random Forest and Gradient Boosting achieve the highest accuracy of 95% and 96%, respectively, followed by SVM at 92%, KNN at 84%, and Decision Trees at 77%. The study also conducts an uncertainty analysis using the R-factor to assess the reliability and consistency of the models. The findings highlight the effectiveness of machine learning in accurately predicting WQI and provide valuable insights for informed decision-making in groundwater management. The study concludes by emphasizing the importance of considering uncertainties in environmental assessments and suggests future research directions to enhance the predictive accuracy and understanding of water quality dynamics.