This study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structures in civil engineering. In this regard, obtaining the estimation of the mechanical properties of HPC is complex energy and time-consuming. Due to this, an observed database was compiled, including 168 datasets for CS and 120 for TS. This database trained and validated two machine learning models: SVR and RT. The models combine the prediction outputs from the meta-heuristic algorithms to build hybrid and ensemble-hybrid models, which include dwarf mongoose optimization, PPSO, and moth flame optimization. According to the observed outputs, the ensemble models have great potential to be a recourse to deal with the overfitting problem of civil engineering, thus leading to the development of more supportable and less polluting concrete structures. This research significantly improves the efficiency and accuracy of predicting vital mechanical properties in high-performance concrete by integrating machine learning and metaheuristic algorithms, offering promising avenues for enhanced concrete structure design and development.