Precise estimate of compressive strength of concrete is very important for the assurance of structure integrity, the best mix composition, and enhancing the quality control during construction. The conventional methods of strength identification after 28-day curing are time-consuming and cause undue delays in key decisions. This study suggests the formulation of a predictive modelling approach based on Multiple Linear Regression (MLR) to predict the compressive strength of concrete. The independent variables including Admixture, Fine Aggregate, Water Cement Ratio, Cement, Crush Sand, Fly-ash, Water, and Coarse Aggregate are included for the development of MLR models. The variable selection techniques such as Enter, Stepwise, and Backward Elimination approaches were employed for evaluating and enhancing model performance. The overall results of developed Enter method MLR model approach demonstrate the correlation R of 0.928 between actual verses predicted compressive of concrete along with higher rate of accuracy R2 of 86.1%. Such predictive modelling approaches make the implementation of smart and robust solutions easier in the field of construction. The proposed MLR predictive modelling approach contributes the body of knowledge to structural engineering and will be helpful for construction practitioners and designers.