The spatio-temporal variations in stream water quality are primarily due to the heat and mass exchanges occurring at the interfaces which are propagative and dilutive along the stretch. The streams are also susceptible for increased pollutant loading from various point and non-point sources causing an imminent depletion of dissolved oxygen (DO) concentration. Though water quality parameters are several, it is pertinent to identify the most critical factors for an easy, reliable and meaningful monitoring system. It is also necessary to have a simplified data-driven model for making necessary predictions for various decision-making processes. The present study investigates the characteristic relationship between various forms of oxygen demands (carbonaceous biochemical oxygen demand—cBOD and sediment oxygen demand—SOD) in a stream with the available DO for a selected stretch of River Bhavani in Tamil Nadu, India. The modelling framework consists of simulating a data-extensive water quality profile using QUAL2K and integrating the outputs for minimizing the required count of parameters using a multi-perceptron feed-forward artificial neural network (ANN) model. The proposed methodology suggested that a minimum of five parameters (inorganic suspended solids—ISS, DO, cBOD, SOD and total nitrogen—TN) are sufficient to simulate the concentration profiles with reasonable accuracy (R2 varies from 0.88 to 0.95). For any local addition of organic-rich influent to the sediment load of the river, there is a corresponding change in the cBOD and DO within the selected stream profile. The results from the present study indicate the advantage of the combined modelling approach for prediction of water quality in case of complex interactions between various oxygen-demanding substrates.