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Abstract. The current study investigated the efficacy of RSM (response surface methodology) and ANN (artificial neural network) models to validate the data and to predict the maximum total phenolic content (TPC) extracted from spent coffee grounds by conventional and microwave assisted extraction (MAE) systems. Experimental conditions such as temperature (20^oC, 40^oC, 60^oC), time (30 min, 60 min, 90 min), and sample mass (0.5 g,1.0 g,1.5 g) for solvent extraction and power (400 W,800 W,1200 W), extraction time (40 s,80 s,120 s) and sample mass (0.5 g,1.0 g,1.5 g) for MAE were considered. A central composite face-centered design has been employed to monitor the combined effect of extraction characteristics, and the results were analyzed using Design Expert Software for RSM and MATLAB R2017b for ANN. The data obtained from the experimental design was fitted to second-order polynomial response surface model which was applied to fit the experimental results obtained by face-centered design. A feed-forward MLP (Multilayer Perceptron) ANN with three or more layers of hidden neurons using backpropagation was used for the validation and testing of ANN-model. The results showed that 60^oC temperature, 90 min time, 0.5 g of sample mass combination gave maximum results with 44.4 mg g-1 of a dry extract of phenolic for solvent extraction whereas, 966.3 W power, 49.0 s time and 0.5g of sample mass extracted 57.5 mg g-1 of a dry extract of phenolic compounds from MAE. MAE yielded higher TPC values than conventional solvent technique with lower time and higher yield. ANN trained network gave the maximum R² value of 0.748 when compared to 0..999 for RSM and average absolute deviation (AAD) values of 2.368% versus 8.105% for RSM. ANN also showed a good agreement between the predicted and the actual TPC values for the selected extraction conditions. The current data shows that the RSM could be a useful mathematical tool to optimize the extraction process while the ANN is a better method for predicting TPC extracted from spent coffee grounds. These research findings could provide an effective guideline, and the results would be a good database to the food industry applications.