In conventional distributed machine learning methods, distributed support vector machines (SVM) algorithms are trained over pre-configured in-tranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM con-verges to the optimal classifier function. Single computer is incapable to train SVM algorithm with large scale data sets. The results of this study are im-portant for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing envi-ronment using SVM will converge to a global optimal classifier in finite iteration size.