Aim: To enhance the Music genre classification using Novel Support Vector clustering Algorithm and Logistic Regression with improved accuracy. Materials and methods: Two groups such as novel Support Vector Machines Algorithm and Logistic Regression are applied. There will be a total of samples analyzed using this approach of 1000 music files. Among this sample dataset, 300 music files[70%] portion of the dataset was used as a training dataset. and 700 [30%] was taken as a testing dataset. Programming experiment was carried out for N=10 iterations for the Novel Support Vector clustering Algorithm and Logistic Regression algorithm respectively. Computation processes were executed and verified for exactness. Each group consists of a sample size of 10 Alpha value of the study's parameters is 2.020, beta value 1.359. The SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Results and Discussions: Novel Support Vector clustering Algorithm shows a high accuracy and homogeneity for Music genre classification, and statistical significance difference is less than 0.001 (p>0.05). Conclusion: The purpose of this essay is to adopt a novel method to the study of the subject Music genre classification. Comparison results show that efficiency Using the novel Support Vector Clustering technique is better than Logistic Regression Classifier for Music genre classification.