This study investigates the application of artificial intelligence in predicting the uniaxial compressive strength of concrete using experimental data. A multilayer perceptron (MLP) neural network was developed using TensorFlow and Keras in Python program. The dataset includes 150 concrete cube samples tested after 28-days of curing, divided into training (70%) and testing (30%) sets. The results show that the proposed model significantly improves prediction accuracy, achieving over 80% recall and maintaining consistent performance through 500 iterations, with an accuracy range of 80-85%. Comparative analysis with algorithms such as SVM, k-NN, RF, DT, and Adaboost indicates that the MLP model provides superior accuracy in predicting concrete strength. The confusion matrix reveals 91% accuracy and 99% precision. Evaluation using MSE, MAE, and RMSE metrics confirms that the MLP model has lower error rates than other algorithms, demonstrating its effectiveness in predicting uniaxial compressive strength.