This study explores the application of a Multilayer Perceptron (MLP) deep learning model to predict the compressive strength of High Performance Concrete (HPC). The dataset, comprising 1030 samples, undergoes meticulous preprocessing, including normalization within a 0-1 range. The MLP model is trained and evaluated, yielding impressive performance metrics such as an R ² score of 0.918201, MSE of 13.76, RMSE of 3.71 MPa, MAE of 4.79 MPa, and a low MAPE of 0.02. Additionally, a comprehensive 10-fold cross-validation ensures the model's robustness and generalization ability. Visualizations in actual versus predicted plots and radar plots provide intuitive insights into the model's predictive prowess. Hyperparameter tuning further refines the model. The study establishes the MLP model as a reliable tool for predicting HPC compressive strength, offering significant implications for enhancing the precision and efficiency of concrete engineering practices.