<p>The modern music business is experiencing a radical change due to the digital platform and the use of artificial intelligence (AI) and social media as the old metrics of success, such as album sells, are being replaced by the streaming numbers and online interactions. This paper will explore the correlation between social media use and perceived music popularity and analyze how AI can be used as an analysis tool that can predict cultural trends in digital music consumption. A mixed-method approach was employed, which involved a quantitative survey of 300 socially active listeners to music with the interview of 15 industry stakeholders. Descriptive statistics, factor analysis, multiple regression, mediation analysis, and time-series correlation were used to analyze quantitative data, and sentiments analysis (TextBlob and VADER) was used to process real-time social media data on large social media platforms. Perceived music popularity has been identified to have about 48 percent of its variance (<i>R</i>² = 0.48) explained by the regression model, with social media engagement, music-sharing behavior, sentiment responsiveness, and trust in AI recommendations as the major predictors. Mediation analysis also shows that the relationship between AI familiarity and perceived popularity partially depends on the trust in AI recommendations, which reinforces the importance of transparency in AI-based systems. According to exploratory sentiment and time-series analysis, prior social media activity, in particular, on TikTok and YouTube, is associated with chart entry (1–3 days). Nevertheless, the accuracy of the predictions that the study can make is not related to the long-term forecasting accuracy but the correlational and classification-based performance measures, and this means that an AI is an instrument that signals early they are successful in music, not a predictor.</p>