 This study proposes a novel hybrid artificial neural network model to accurately predict the compressive strength of rice huskash RHA concrete. The model was trained using 192 data points with six input parameters including age cement rice huskash superplasticizer aggregate and water. The model outperformed four other models in terms of predictive accuracy as measured by the coefficient of determination, R2, variation adjusted factor, DAF, root mean square error, RMSE, and mean absolute error, MAE. Additionally, the model identified age as the most significant parameter for predicting the compressive strength of RHA concrete. This article was authored by Chuanchili, Qianxiang Mei, Daniel Diaz, and others.