One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (Tc) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions . This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process.