Predictive modeling is fundamental to advancing scientific and engineering disciplines, offering critical insights that enhance innovation and improve decision-making processes. However, accurately forecasting the compressive strength of High-performance Concrete (HPC) is still difficult, even with major advances in material science and engineering. This is mostly due to the complexity of its components, the fluctuating environmental factors, and the shortcomings of conventional testing techniques. Furthermore, there is lack of in-depth comparative studies that evaluate multiple advanced machine learning models using various performance metrics. Additionally, the development of a standardized interface to enhance the flexibility and applicability of top-performing models has not been sufficiently explored. By examining the effectiveness of eleven prediction models including techniques like Linear Regression, Random Forest, Support Vector Machines, CatBoost, and XGBoost, this study seeks to address these issues. A carefully selected dataset from earlier research was divided into training and testing sets. Key performance indicators like the R2 coefficient, Mean Absolute Error (MAE), and Root Mean Squared Logarithmic Error (RMSLE) were used to assess the models. Results indicate that the Cat Boost Regressor demonstrated superior predictive accuracy, with an R2 value of 0.96 and notably low errors (MAE = 2.2, RMSLE = 0.12) on the testing set, significantly outperforming conventional models like Linear Regression. These findings underscore the potential of advanced models like Cat Boost Regressor to completely transform HPC Compressive Strength prediction, promoting more effective material design and environmentally friendly building techniques in both theoretical and real-world applications.