The emergence of streaming services, e.g., Spotify, has changed the way people listen to music and the way musicians achieve fame and success. Classical music has been the backbone of Western media for a long time, but Spotify has introduced the public to a much wider variety of music, also opening a new venue for musicians to gain exposure. In this paper, we use open-source data from Spotify and Musicbrainz databases to construct collaboration-based and genre-based networks. Our goal is to find the correlation between various features of each artist, their current stage of a career, and the level of their success in the music field. We build regression models using XGBoost to first analyze correlation between features provided by Spotify. We then analyze the correlation between the digital music world of Spotify and the more traditional world of Billboard charts. We find that within certain bounds, machine learning techniques such as decision tree classifiers and Q-based models perform quite well on predicting success of musicians from the data on their early careers. We also find features that are highly predictive of the artist’s success. The most prominent among them are artists’ collaboration counts and the span of their career. Our findings also show that classical musicians are still very centrally placed in the general, genre agnostic network of musicians. Using these models and success metrics, we make suggestions that aspiring musicians can rely on considering which aspects of their career should be improved to increase their success measures in both Spotify and Billboard charts.