Alloys containing four or more principal alloying elements in near equal atomic percentage, so called High Entropy Alloys (HEA), break down the solvent-solute relationship generally seen in traditional alloys. This creates a vast and unpredictable design space, unfeasible to explore purely experimentally or through computational simulations. This thesis aims to present a newly developed machine learning based phase prediction methodology for HEA alloys, the empirical design and characterization of a Zr-containing HEA system, and a performance comparison of the developed methodology and established empirical method at predicting the impact of Zr on the phase composition with experimental results as a reference. Findings show that the developed phase prediction methodology, which leverages a convolutional neural network, could predict the formation of a secondary BCC solid solution phase within the high entropy alloys caused by the addition Zr and intermetallic compound formation, while the empirical parameters failed to do so.