The study explores dataset preparation, machine learning (ML) model training, interpretation, and life cycle assessment (LCA) to predict and enhance the performance and sustainability of rubberized concrete. A large dataset comprising 1209 collected samples with nine input features was used to train and evaluate six machine learning models. Among the six models, the Light gradient boosting machine (LightGBM) model achieved the highest prediction accuracy on the testing dataset, with an R² value exceeding 0.96, a MAPE of 8.31 %, a MAE of 2.36 MPa, and a RMSE of 3.25 MPa. The SHAP algorithm was used to interpret predictions and identify key factors influencing compressive strength. Rubber content and water-to-cement ratio reduced strength, while longer curing time, more superplasticizer, higher fine aggregate content, and a greater silica fume-to-cement ratio improved it. Coarse aggregate, crumb rubber size, and cement content had minimal impact. Optimal performance was achieved with: Rubber content <55.92 kg/m³, w/c ratio <0.4, curing time >26 days, superplasticizer >3.7 kg/m³, fine aggregate >642 kg/m³, and silica fume/cement ratio >2.5 %. LCA results show that, although rubberized concrete offers no clear advantage over conventional concrete in cost, carbon, or energy, the lower strength and higher superplasticizer use of rubberized concrete lead to greater strength-normalized impacts, and therefore its value lies more in waste recycling and toughness than in strength-based sustainability. The unique advantage of this research lies in the development of a ML-LCA integration framework that synthetically balances performance prediction and sustainability assessment of rubberized concrete, with delivering actionable mix design recommendations, identifying key and low-impact variables, and revealing trade-offs among strength, cost, and environmental performance.