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Music has a significant impact on people's lives. Music brings people of the same interests together and holds people together. The goal of our study is to develop a machine-learning system that outperforms existing algorithms for predicting music genres. The key goal is to get a high level of accuracy such that the model appropriately classifies new music into its genre. We used the GTZAN dataset, which is the most widely used public dataset for music genre detection and evaluation in machine listening research. We input the audio file and the classifier determines its genre and displays it as output. The audio files are categorized on the basis of their frequencies as well as other aspects with respect to time. The Mel Frequency Cepstral Coefficient (MFCC), taken from the dataset, is utilized for constructing the feature vectors for the classifiers. [ABSTRACT FROM AUTHOR]