The elements on the surface of stars carry a permanent snapshot of the star formation and chemical evolution history of a galaxy. When modern models of galactic chemical evolution are compared to these snapshots, it should be possible to discern the chemical evolution process in a galaxy. However, current models are too time-intensive to evaluate with proper statistical methods, which require many iterations of the model within its parameter space to produce a probability distribution of starting parameters that best fit the observations. As one proposed solution, we aim to emulate the Galaxy Assembly with Merger Trees for Modeling Abundances (GAMMA) model through the use of Gaussian process regression, then compare the results to newly available observational data with Markov Chain Monte Carlo methods. By training a Gaussian process based emulator with numerous training GAMMA samples generated from a sparsely sampled set of input parameters, we seek to greatly reduce the computational time required to produce chemical evolution predictions from GAMMA. Given this, we expect to use this emulator model (GAMMA-EM) in conjunction with Markov Chain Monte Carlo to obtain a set of GAMMA input parameters that produce the best model fit to newly available observational data. This will likely improve our current understanding of the chemical evolution process in our galaxy and many others.