Groundwater quality in arid and semi-arid regions faces significant risks due to factors like over-extraction, pollution, and climate change. Traditional models often struggle to provide accurate and actionable insights, hindering effective decision-making. This chapter presents an innovative technique to improve groundwater quality prediction through the integration of Explainable Artificial Intelligence (EAI) with the use of Machine Learning (ML) and Deep Learning (DL) techniques. Transparency in the decision-making process is another benefit of the integrated approach, which helps stakeholders understand the fundamental causes of variations in groundwater quality. The chapter addresses the development of ML and DL models, the use of EAI to interpret and validate models, and the particular difficulties of managing groundwater in arid and semi-arid areas. This chapter demonstrates the practical benefits of this innovative approach through the case studies, providing a robust framework for improving groundwater quality prediction and management in water-scarce regions.