Computational methods have been explored for predicting agents that produce therapeutic or adverse effects in cardiovascular and hematological systems. The quantitative structure-activity relationship (QSAR) method is the first statistical learning methods successfully used for predicting various classes of cardiovascular and hematological agents. In recent years, more sophisticated statistical learning methods have been explored for predicting cardiovascular and hematological agents particularly those of diverse structures that might not be straightforwardly modelled by single QSAR models. These methods include partial least squares, multiple linear regressions, linear discriminant analysis, k-nearest neighbour, artificial neural networks and support vector machines. Their application potential has been exhibited in the prediction of various classes of cardiovascular and hematological agents including 1, 4-dihydropyridine calcium channel antagonists, angiotensin converting enzyme inhibitors, thrombin inhibitors, AchE inhibitors, HERG potassium channel inhibitors and blockers, potassium channel openers, platelet aggregation inhibitors, protein kinase inhibitors, dopamine antagonists and torsade de pointes causing agents. This article reviews the strategies, current progresses and problems in using statistical learning methods for predicting cardiovascular and hematological agents. It also evaluates algorithms for properly representing and extracting the structural and physicochemical properties of compounds relevant to the prediction of cardiovascular and hematological agents.