Abstract: In the recent past, the applications of artificial intelligence in data analysis and prediction has indisputably increased. Fuzzy Logic (FL) and Artificial Neural Networks (ANN) find extensive applicability with the aim of achieving human-like or superior performance. FL is used in the fields of consumer products, industrial process control, medical instrumentation and portfolio selection while ANN applications include system identification and control, decision making, patter recognition, sequence recognition, visualization, data mining and financial applications. FL and ANN have the ability to learn from its environment and to improve its performance through learning. This study presents application of FL and ANN in predicting the 28 day compressive strength of self-compacting concrete containing mineral and chemical admixtures. This becomes extremely advantageous in predicting the compressive strength of Self Compacting Concrete (SCC) mixes containing binary, ternary or quaternary blends. The results obtained from the fuzzy logic prediction model and from ANN training, testing and validation were compared with the experimental values from the literature and its performance was evaluated in terms of root mean square error, correlation coefficient, coefficient of performance, mean absolute error and percentage mean relative error.