This study investigates the applicability of machine learning algorithms for predicting the compressive strength of cement mixtures with zeolite. The research compares the performance of four predictive models—Elastic Net regression, Support Vector Machines (SVM), Multilayer Perceptron (MLP) neural networks, and Decision Trees—trained on experimentally obtained data describing mix composition and curing conditions. The input features included zeolite percentage, water-to-cementitious-material ratio, curing time, cement mass, and zeolite content. The output variable was compressive strength. Among the evaluated models, the SVM algorithm exhibited the optimal generalization capability, attaining the minimal prediction error on the validation set while sustaining elevated correlation between actual and predicted values. The MLP neural network demonstrated the optimal fit to the training data, however, this was achieved at the expense of heightened sensitivity to overfitting. Decision trees demonstrated robust training efficacy but exhibited diminished generalization capabilities, while the linear elastic net model encountered challenges in replicating the nonlinear characteristics of the material system. The study corroborates the viability of nonlinear machine learning models in facilitating the design and optimization of zeolite-enhanced cementitious mixtures. These findings signify a significant stride towards data-driven modeling in the field of construction materials engineering, thereby facilitating enhanced prediction of mechanical performance with minimized experimental effort. The study also underscores avenues for future exploration, encompassing model hybridization, multi-output prediction frameworks, and integration with optimization algorithms for automated mix design.