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In this study, we use Artificial Neural Networks (ANN) and Multiple Regression Analysis to evaluate the prediction of two crucial self-compacting concrete properties: compressive strength and split tensile strength. It was possible to create four different datasets, each of which had different concrete mix proportions along with their respective ages in days, compressive strengths (MPa), and split tensile strengths (MPa). Separate ANN models and Regression models were trained and tested using these datasets. As a gauge of prediction accuracy, Mean Squared Error (MSE) was used to assess the performance of the models. This study offers insightful information on the application of multiple regression analysis and artificial neural networks to forecast the characteristics of self-compacting concrete using GGBS and Alccofine. Here Alccofine functions as an additive and GGBS acts as a partial substitute for cement at 0 to 60% with a fluctuation of 10%. The outcomes highlight the potential of neural networks as a tool for concrete mix design optimization and quality control since they can capture complex correlations between input variables and concrete strength.