This research investigates the rheological and mechanical characteristics of Self-compacting Concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as a substitute for coarse aggregates. The study aims to identify the optimal proportion of WRTA to achieve the best performance in SCC. Linear regression (LR) and extreme gradient boosting (XGBoost) machine learning models are employed to predict the rheological and mechanical properties of the rubberized SCC. The study replaces conventional coarse aggregates with WRTA at 0%, 5%, 10%, and 20% to determine the optimal substitution ratio. The results show that increasing the WRTA content leads to a decrease in workability and hardened qualities. A 10% WRTA substitution is feasible for producing SCRC, but the optimal result is achieved with a 5% substitution, which reduces environmental impacts and efficiently manages rubber tire waste. The study also finds that after 28 days, a 10% WRTA substitution results in a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. XGBoost outperforms LR in terms of predictive accuracy, with higher R² values, confirming its efficacy in delivering more accurate predictions. The research contributes to the development of more sustainable and cost-effective concrete mixtures by optimizing the use of recycled rubber tire aggregates.This research investigates the rheological and mechanical characteristics of Self-compacting Concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as a substitute for coarse aggregates. The study aims to identify the optimal proportion of WRTA to achieve the best performance in SCC. Linear regression (LR) and extreme gradient boosting (XGBoost) machine learning models are employed to predict the rheological and mechanical properties of the rubberized SCC. The study replaces conventional coarse aggregates with WRTA at 0%, 5%, 10%, and 20% to determine the optimal substitution ratio. The results show that increasing the WRTA content leads to a decrease in workability and hardened qualities. A 10% WRTA substitution is feasible for producing SCRC, but the optimal result is achieved with a 5% substitution, which reduces environmental impacts and efficiently manages rubber tire waste. The study also finds that after 28 days, a 10% WRTA substitution results in a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. XGBoost outperforms LR in terms of predictive accuracy, with higher R² values, confirming its efficacy in delivering more accurate predictions. The research contributes to the development of more sustainable and cost-effective concrete mixtures by optimizing the use of recycled rubber tire aggregates.