Concrete’s compressive strength is pivotal in civil engineering, ensuring structured stability alongside durability. This study investigates a dataset containing 1030 instances with eight quantitative intake factors to consider: age, coarse aggregate, fine aggregate, super plasticizer, slag, ash, and water—alongside compressive strength. Domain analysis underscores the importance of each constituent in concrete formulation. Initial checks confirm dataset integrity, with duplicates removed and no missing values. Exploratory data analysis (EDA) reveals variable distributions and relationships, informing preprocessing and model selection. Following preprocessing and feature scaling, seven regression models such as Support vector regression, Decision Tree, Gradient boosting, K Nearest Neighbor, Linear Regression, Random Forest, and XG Boost Regressor—are evaluated. The XG Boost Regressor emerges as the top performer, yielding the highest R2 score and lowest MSE, RMSE, and MAE. Key influencers of compressive strength include concrete age, cement quantity, water content, slag amount, and superplasticizer quantity, with coarse aggregate, fine aggregate, and fly ash exerting minor effects. Despite challenges like outlier handling and hyperparameter tuning, this study offers valuable insights into accurately predicting concrete strength. The findings contribute to improving construction practices, facilitating the design of resilient and sustainable structures.