This study investigates the application of Artificial Neural Networks (ANNs) for rainfall-river modelling in the Oramiriukwa River, located in Owerri, Imo State, Nigeria. The study utilizes daily streamflow data from the Ulakwo station (1978–1988) alongside corresponding rainfall and temperature data for Imo State, obtained from the Nigerian Meteorological Agency (NiMet). A series of Feedforward Multi-Layer Perceptron (MLP) models were developed and tested using MATLAB, with performance evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Among the models, Model 4 ([15]) delivered the best results, achieving an R² of 0.9158, MSE of 0.1294, and RMSE of 0.3597, demonstrating its effectiveness for streamflow prediction in the Oramiriukwa River. Model 2 ([30, 15, 5]) also showed good performance (R² = 0.9029), but its increased complexity suggested a potential risk of overfitting. Model 1 ([20, 10]) yielded lower predictive accuracy, highlighting the need for more complex architectures or additional input features to improve ANN performance for hydrological applications. These results confirm the effectiveness of ANNs in modelling nonlinear hydrological processes and suggest their potential for improving streamflow prediction in similar river basins. This study contributes to the growing use of data-driven methods in hydrological modelling in Nigeria and offers a foundation for future work aimed at enhancing the accuracy and robustness of ANN models.