In this study, artificial neural network (ANN) and fuzzy logic (FL) models have been developed for predicting the compressive strength (f c ) and dynamic modulus of elasticity (E d ) of the crushed tile concretes (CTC) exposed to elevated temperatures. Some relationships are established between chosen inputs and outputs by developing and testing a multi-layered feed forward ANN and FL trained with the back-propagation algorithm. First of these relationships is established between the outputs as f c of CTC after being exposed to elevated temperatures and the inputs as exposed temperature (T), crushed tile aggregate (CT) and crushed stone II (CSII) contents of concrete. The second one is the relationship between E d of concretes and the same inputs. In this aim, concrete specimens are produced by CT replacing 16-31.5 mm coarse aggregate at the ratios of 0%, 10%, 25%, 50%, 75% and 100%. Concrete specimens are exposed to 20, 150, 300, 400, 600, 900 and 1200 °C high temperatures corresponding TS EN 1363-1 after an initial 28 day curing period. After heating, the specimens are slowly air-cooled to the room temperature and then E d and f c of concretes were determined. Experimental results are also predicted by constructing models in ANN and FL methods. In the models, the training and testing results have shown that ANN and FL methods have strong potential for predicting the f c and E d of crushed tile concretes exposed to elevated temperatures.