<p>Ultra-high-performance concrete (UHPC) has emerged as an advanced construction material due to its superior strength, durability, and resistance to aggressive environmental conditions. However, predicting its compressive strength is complex because UHPC properties depend on multiple interacting material and workability parameters. Therefore, the present study aims to develop an efficient machine learning-based predictive framework for estimating the compressive strength of UHPC. A comprehensive database consisting of 360 experimental records was compiled from previous studies. Eight key input parameters, fly ash, silica fume, T500 flow time, maximum spread diameter, J-ring value, h2/h1 ratio, V-funnel time, and temperature were considered as predictors. Two hybrid machine learning models were developed using Gaussian Process Regression (GPR) optimized with metaheuristic algorithms, namely the Snow Geese Algorithm (SGA) and Horse Herd Optimization (HHO). The dataset was normalized and divided into training, validation, and testing subsets. Model performance was evaluated using several statistical indicators including root mean square error, mean absolute error, correlation coefficient, weighted mean percentage absolute error, and variance accounted for. The results demonstrate that both hybrid models provide reliable predictions of UHPC compressive strength, while the GPR–SGA model achieved slightly higher accuracy than the GPR–HHO model. Furthermore, explainable artificial intelligence techniques such as SHAP analysis and Partial Dependence Plots were applied to interpret the influence of input variables. The analysis revealed that temperature and silica fume content have the most significant impact on compressive strength prediction. The proposed modeling framework provides a practical decision-support tool for engineers to estimate UHPC strength efficiently, optimize mixture design, and reduce the need for extensive laboratory experimentation.</p>