This chapter includes comprehensive and self-developed techniques of advanced regression and ensemble learning methods for predicting concrete compressive strength approaches. The study starts with data preprocessing of the UCI “Concrete Compressive Strength” dataset that includes cleaning, normalization, and transformation of the data and then proceeds with the creation of four models including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) with the best hyperparameters achieved using GridSearchCV and cross-validation. The three types of ensemble methods that the experiment elaborates on are stacking, the mean of the classifiers, and soft voting. A quantitative analysis of the results in terms of MAE, MSE, RMSE, R2, and MAPE shows that even single models, especially XGBoost, have good accuracy; however, the stacking ensemble model is the most accurate and reliable in predicting the target variable due to its ability to capture intricate and nonlinear relationships between the input features. The study demonstrates the applicability of the advanced ensemble techniques in the concrete mix design and improves the quality assurance in construction.