2.1 Introduction Traditionally, a concrete mix is ​​designed based on the requirements and recommendations of the code using empirical values ​​obtained from previous experiences. The compressive strength of concrete is determined by performing standard uniaxial compression tests on standard 7- and 28-day-old cylindrical specimens, following the standard procedure, and the test values ​​are reported in accordance with ASTM and ACI standards. If the strength value obtained from the test is less than the required strength after 28 days from the date of placing the concrete, the entire concrete mix design process must be repeated until the required strength value is reached, which takes time and money. Numerous test samples need to be created with different proportions of the mixture ingredients to achieve the required strength and is an iterative process. Therefore, every mix designer wants a tool or methodology to predict the required concrete compressive strength at the time of design, before pouring the concrete. As we know, the relationship between compressive strength and mixture ingredients is complex and highly nonlinear. Data scientists, researchers and engineers are trying to develop different approaches using regression function for accurately predicting the compressive strength of concrete. Recently, data mining tools are becoming more popular and reliable methods than others for predicting the compressive strength of concrete. The following section reviews and discusses some of the popular, relevant and effective data mining tools developed so far, for predicting 28-day compressive strength. of concrete.2.2 Multiple Regression ModelThe first popular regression equation used in predicting compressive strength......middle of paper......for plasticizer, fine aggregates and coarse aggregates. The least squares method is used to estimate the regression coefficients in the above model. Many researchers have used multiple regression models to improve the accuracy of concrete strength prediction. It is still a popular model because the model, when adapted, can predict the required value more quickly than other modeling techniques and can be easily implemented in computer applications. By performing correlation analysis, it also provides in-depth knowledge on the key factors influencing the 28-day compressive strength of concrete. Although it performs better where there are few independent variables, it performs poor modeling when the number of independent variables is larger and the relationship between independent variables and the dependent variable becomes complex and highly nonlinear.