Based on the historical data, this research suggests a prediction model to calculate the strength of concrete. This study's main contribution is the reduction of the discrepancy between the concrete strength measurement model's projected value and actual value. In the suggested paradigm, artificial neural networks are used to do this. The effectiveness of the neural network depends on the weight values which connects the neurons in the network. However, determination of optimal weight value is difficult task. Therefore, bio-inspired honey badger algorithm is deployed for it. This algorithm based on the ingenious honey badger foraging techniques foraging behaviour of honey badger and provides better exploration rate to find the optimal solution based on the objective function over the algorithms. In the proposed model, the objective function used is the root mean square error and its value minimized to lessen the discrepancy between the projected and real value. The standard dataset is taken under consideration and various performance metrics such as Standard deviation of error (SDE), normalized mean absolute error (NMAE), mean absolute percent error (MAPE), and root mean square error and convergence rate are measured to validate the performance of the proposed prediction model. It is found that the proposed model achieves lower value of these performance metrics over the existing model based on ANN. Besides that, convergence rate graph shows that the honey badger algorithm is quickly determine the optimal weight values.