Regional and culturally-based classification of traditional music provides a useful framework for analyzing and understanding the diverse musical traditions of different cultures and regions. In this study, a categorization scheme for Indian folk music is presented to support music analysis like discovery and understanding of the character of musical traditions, their similarity, and difference. Since India has a lot of different languages and diversified musical culture, it is challenging for human beings to know all types of music, so it is very challenging for a single human being to classify Indian music into various genres. For the proposed system, many well-known Indian music genres, including Assamese, Kannada, Kashmiri, Marathi, Uttarakhandi, Bolly-Rap, Ghazal, Garhwali, Bhajan, Bolly-Romantic, Sufi, Bhojpuri has been selected. Extracting the various features like spectral shape features and perceptual features and by using different feature combinations, apply and contrast the performance involving various machine learning algorithms like kNN and SVM for the classification task. Out of these both classification methods, SVM gives the best accuracy of 74.97% than the kNN which gives the highest accuracy of 72.98% for the nearest neighbor 3.