Concrete is a highly complex composite construction material and modeling usingcomputing tools to predict concrete strength is a difficult task. In this work an effortis made to predict compressive strength of concrete after 28 days of curing, usingArtificial Neural Network (ANN) and Genetic programming (GP). The data for analy-sis mainly consists of mix design parameters of concrete, coefficient of soft sand andmaximum size of aggregates as input parameters. ANN yields trained weights andbiases as the final model which sometime may impediment in its application at oper-ational level. GP on other hand yields an equation as its output making its plausibletool for operational use. Comparison of the prediction results displays the result themodel accuracy of both ANN and GP as satisfactory, giving GP a working advantageowing to its output in an equation form. A knowledge extraction technique used withthe weights and biases of ANN model to understand the most influencing parametersto predict the 28 day strength of concrete, promises to prove ANN as grey box ratherthan a black box. GP models, in form of explicit equations, show the influencing pa-rameters with reference to the presence of the relevant parameters in the equations.