Transformation of rainfall to runoff is a complex hydrological phenomenon involving various interconnected processes. Besides, the distribution of rainfall and basin characteristics are not uniform across time and space leading to a poor understanding of the process. Hydrologists have been using various hydrological models to understand transformation of rainfall into runoff. Conceptual models developed in the 1960s represent various individual components of hydrological cycle via interconnected conceptual elements, thus model various aspects of the hydrological cycle. On the other hand, data-driven models such as Artificial Neural Networks (ANNs) are widely regarded as universal approximators due to their ability to model many complex problems. Very few studies reported the application of a widely used conceptual model, Sacramento Soil Moisture Accounting model (SAC-SMA), in the Indian river basins context. Considering that the hydrological cycle is very complex and may never be fully understood in detail, conceptual models like Sacramento Soil Moisture Accounting model (SAC-SMA) can be integrated with data-driven models which can take care of poorly described and understood aspects of hydrological modelling. In this study, a hybrid rainfall-runoff model was developed and applied over the Godavari river basin in India at multiple spatial scales for capturing the spatial variations in model inputs and catchment charateristics.The hybrid model by virtue of the semi-distributed configuaration and addition of ANN component led to improved simulations of streamflow in comparison to the standalone SAC-SMA model.