Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, including cross-validation plus (CV+) and conformalized quantile regression (CQR) methods. Results demonstrate that LGBM and XGBoost outperform RF, improving R² by 4.5% and 5.7% and reducing Root-mean-square Error (RMSE) by 24.6% and 24.8%, respectively. While CV+ yielded narrower but constant intervals, CV+_Gamma and CQR provided adaptive intervals, highlighting trade-offs among precision, adaptability, and coverage reliability. The integration of CP offers a robust framework for uncertainty quantification in SCC strength prediction and marks a significant step forward in ML applications for concrete research.