This work presents a novel approach for defect based fatigue failure characterization using artificial intelligence (AI). An artificial neural network (ANN) is trained on experimentally determined data that is highly relevant in terms of fatigue. Load stress, hardness and killer defect size are the three main parameters defined as input arguments. Fatigue testing either reveals failure or non-failure, which represent the two possible output variables. Thus, every specimen subjected to this research work generates at least one data set. After total fracture occurs at a certain load level, killer defect size is evaluated by fracture surface analysis. The architecture as well as hyperparameters of the neural network are optimized by K-fold cross validation in order to obtain best prediction accuracy. Eventually, a conservative mean fatigue failure prediction accuracy of 91.6% is achieved. This unprecedented methodology is pioneering to predict fatigue failure without the need for extensive, error-prone, use of complex assessment methodologies and associated comprehensive expensive material testing. Without any expert-knowledge of evaluation procedures, developed AI-approach enables quick and reliable prediction of fatigue failure of machined components based on elementary key figures and shows prospective ways to revolutionize fatigue characterization.