The contemporary electronic age is redefining the dissemination of music with computerized methods that can collect and analyze large amounts of data from the web. The objective of this research is to use various machine learning and statistical techniques to examine the acoustic characteristics of songs. We have experimented with several models including random effects, since we are particularly interested in understanding the determinants of a song's popularity and acquiring more insights into these elements. Our study will be beneficial for those seeking to predict the achievement of new goods in the music industry since it delineates the essential aspects that affect a song's popularity. This study seeks to elucidate the correlation between a song's auditory characteristics and its popularity, as well as to discover the key aspects that lead to a song's popularity. The proposed system offers an essential contribution to our comprehension of the music industry and its future course in this fascinating area of research. The work proposes the use of order-limiting methods in K-means clustering to achieve stable outcomes and improve accessibility, which might decrease categorization mistakes in activities involving measure identification. This approach also enables the automated extraction of the musical framework of popular song content.