Exploring qualitative measures of any waterbody is as vital as quantitative analysis for the sustainability of our water resources. Thus, examining the dynamics of spatiotemporal behaviour of dominant Water Quality Parameters (WQPs) along any waterbody is indeed critical for proposing the appropriate water resource management. This study aims to create a Machine learning model for mapping the dominant optical and non-optical WQPs such as Electrical Conductivity (EC), pH, Temperature (Temp),

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... l Dissolved Solids (TDS), Silicon Dioxide (SiO2) and Dissolved Oxygen (DO) using satellite data. However, the association between WQPs and satellite data is strenuous to model precisely using simple regression theory. In this context, we developed remote sensing-based Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) regressor with optimized Hyperparameters to understand the spatiotemporal variations of WQPs using Landsat-8 imageries. We evaluated six years of satellite data for the geographical area covering from Ankinghat to Chopan (20 sampling stations under Central Water Commission (CWC), Middle Ganga Division (MGD) II) for characterizing the trends of dominant Physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the remote sensing reflectance for the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88- 0.98 for XGBoost and 0.72-0.97 for MLP have generated with bands B1-B4 and their ratios more consistent. Indeed, our findings recommend fewer in-situ measurements to generate reliable Landsat-8 based ML models to estimate Spatio-temporal variations of Physico-chemical and biological WQPs to facilitate better management of our waterbodies.