This study presents a comprehensive approach to predicting concrete compressive strength (CS) using machine learning (ML) models, with a focus on accuracy, efficiency, and practical applicability. The research analyzed 1030 experimental CS data points ranging from 2.33 to 82.60 MPa, collected from previous studies. The ML models included both non-ensemble and ensemble types, such as regression-based, evolutionary, neural network, fuzzy-inference-system, adaptive boosting, random forest, and gradient boosting. The input parameters included cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with CS as the output. The study evaluated the models using visual and quantitative methods, including k-fold cross-validation, and conducted a sensitivity analysis using SHAP to understand the impact of each input variable on CS prediction. The CatBoost model was found to be the most accurate, with the highest R² of 0.966 and the lowest RMSE of 3.06 MPa. A Graphical User Interface (GUI) was developed to enable quick and cost-effective CS prediction, replacing traditional computational or experimental tests. The study also highlights the importance of using ML models for predicting CS, as traditional methods are time-consuming and costly. ML models can handle complex, non-linear relationships between concrete components and strength, leading to more accurate predictions. The research demonstrates that ML models, such as CatBoost, XGBoost, and RF, outperform traditional regression models in terms of accuracy and reliability. The study emphasizes the need for sustainable alternatives to Portland cement, such as waste and recycled materials, to reduce environmental impact and carbon emissions. The findings suggest that ML models can significantly improve the accuracy of CS predictions, optimize concrete mix designs, and enhance the sustainability of construction materials. The study provides a practical solution for concrete designers to predict CS efficiently, reducing the need for extensive experimental testing. The results indicate that ML models, particularly ensemble models like CatBoost, offer a reliable and efficient method for predicting CS, with high accuracy and low error indices. The study also highlights the importance of data normalization and hyperparameter tuning to improve model performance. Overall, the research contributes to the field of structural engineering by demonstrating the effectiveness of ML models in predicting concrete compressive strength, with practical applications for improving design efficiency and sustainability.This study presents a comprehensive approach to predicting concrete compressive strength (CS) using machine learning (ML) models, with a focus on accuracy, efficiency, and practical applicability. The research analyzed 1030 experimental CS data points ranging from 2.33 to 82.60 MPa, collected from previous studies. The ML models included both non-ensemble and ensemble types, such as regression-based, evolutionary, neural network, fuzzy-inference-system, adaptive boosting, random forest, and gradient boosting. The input parameters included cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with CS as the output. The study evaluated the models using visual and quantitative methods, including k-fold cross-validation, and conducted a sensitivity analysis using SHAP to understand the impact of each input variable on CS prediction. The CatBoost model was found to be the most accurate, with the highest R² of 0.966 and the lowest RMSE of 3.06 MPa. A Graphical User Interface (GUI) was developed to enable quick and cost-effective CS prediction, replacing traditional computational or experimental tests. The study also highlights the importance of using ML models for predicting CS, as traditional methods are time-consuming and costly. ML models can handle complex, non-linear relationships between concrete components and strength, leading to more accurate predictions. The research demonstrates that ML models, such as CatBoost, XGBoost, and RF, outperform traditional regression models in terms of accuracy and reliability. The study emphasizes the need for sustainable alternatives to Portland cement, such as waste and recycled materials, to reduce environmental impact and carbon emissions. The findings suggest that ML models can significantly improve the accuracy of CS predictions, optimize concrete mix designs, and enhance the sustainability of construction materials. The study provides a practical solution for concrete designers to predict CS efficiently, reducing the need for extensive experimental testing. The results indicate that ML models, particularly ensemble models like CatBoost, offer a reliable and efficient method for predicting CS, with high accuracy and low error indices. The study also highlights the importance of data normalization and hyperparameter tuning to improve model performance. Overall, the research contributes to the field of structural engineering by demonstrating the effectiveness of ML models in predicting concrete compressive strength, with practical applications for improving design efficiency and sustainability.