The main aim of this paper was to computationally simulate the cardiac ischemia employing Finite Element Method (FEM) and detect its presence and localization using data mining approach. A simplified heart-torso model was created based on computed tomography (CT) images, with performed segmentation of the heart (17 zones). Ischemic and non-ischemic cardiac beats were simulated in different zones with aim to create a virtual database which was used for data mining. Using the virtual database, we trained several classifiers and tested them for the purpose of ischemic beat detection based on the body surface potentials map (BSPM). If the ECG is classified as ischemic by the first stage classifier, potentials were processed by the second stage data mining model, which predicted the location of the ischemic area. The use of the second stage classifier, which located the ischemia in one of the heart’s segments created in the FEM model, goes beyond the current state of the art. Thus, the proposed approach is improved solution which can instantly allow clinicians to implement an adequate treatment strategy in future.