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The estimation of stream discharge is an essential component of planning and decision-making. It is highly correlated with many development activities involving water resources. The study of transportation of sediments in the rivers will help us to develop policies and plans for soil conservation, flood control, irrigation, navigation, and aquatic biodiversity problems. Using data-driven models such as Artificial Neural Networks (ANNs), modeling of streamflow and sediment transport is frequently adopted due to their applicability and problem-solving ability. This study has used three training algorithms such as Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg-Marquardt (LM) to simulate the streamflow and Suspended Sediments Concentration (SSC). After optimizing the best training algorithm based on the model efficiency parameters, L-M based-ANN model has been used to predict streamflow for two years and the modeling of suspended sediments was validated with the help of observed data. The result shows that the simulated results tracked the streamflow as well as SSC with the desired accuracy based on the model efficiency parameters such as coefficient of Determination (R2), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Root Mean Square Deviation (RMSD). The study's outcomes reveal that in the streamflow the concentration of suspended sediments is significantly affected by the base rock material, glaciers covered by debris, and moraine-laden ice. The transportation of the sediments is high in the Alaknanda basin as compared to the other basins and the previous studies. This might happen due to the severe anthropogenic activities in the surrounding basin.