Mental illness can be the reasons of extreme behavioral, emotional, and physical health issues. Majorly there are 4 mental disorders which are based on disposition, uneasiness, identity and insanity. Most of the times symptoms pertaining to these mental diseases are common. But remedies on each mental disorder is different. Due to the commonly existing symptoms of each disease, identifying the exact type of mental disorder is difficult. To smoothen the process of identifying exact mental disorder we use machine learning algorithms. The algorithms are executed on 1020 patient records containing nine parameters which show mental status such as l consciousness level, general behavior, and so on. To predict the exact type of mental clutter/disorder, KNN and SVM are implemented using 80:20 ratio of training-to-testing data. SVM proved to be more accurate that is low misclassification error and greater recall. The accuracy of prediction is steady for 300 to 1020 records.