Abstract: This study delves into how AI can be applied within water and environmental engineering research, particu- larly emphasizing the utilization of machine learning models to advance the accuracy of water quality predictions in the Delaware River. Through an analysis of time series data spanning from 2020 to 2022 and the utilization of exploratory data analysis methods, this investigation scrutinizes various elements influenc- ing the dynamics of water quality. For water quality time series analysis, identifying changes in long-term trends is important, yet identifying specific change-points is also important[3]. A strong correlation is notably detected between levels of dissolved oxygen and recorded temperatures. Leveraging this correlation, an intricate polynomial regression model is crafted to forecast dissolved oxygen concentrations based on expected temperature values. This predictive model not only clarifies the inherent link between dissolved oxygen and temperature but also offers insights into projecting future dissolved oxygen levels in the Delaware River, considering anticipated temperature fluctuations. These findings hold significant promise, potentially enhancing ecologi- cal evaluations and the development of impactful management strategies, specifically designed for water quality monitoring and conservation efforts within the Delaware River basin. Hence, eval- uation of water quality of groundwater is extremely important to prepare for remedial measures[1].