Machine Learning (ML) applications in material science is an active area of research due to its accurate predictive capabilities while also being generalizable. Experimentation where a physical model of the problem is constructed has been widely used in the past and is still used today but is very costly in time and capital. Simulation using virtual models can be much more efficient than experimental modeling but still takes time to construct and solve computationally. ML uses statistics and data science methods to predict a variety of different problem configurations quickly while remaining accurate. While becoming an increasingly popular method, different ML model types have their own advantages and disadvantages. In this work, ML models are trained to predict the thermal expansion of a hollow sphere so that these advantages and disadvantages may be studied. Six ML model types were explored by training on instances of data generated from Finite Element Analysis (FEA) data then compared using error metrics. Sample size analysis was completed to determine required amounts of input data to produce an accurate model for each ML type. A noise study was conducted to observe how each model type would react to varying levels of noise added to the data. Sensitivity analysis was carried out on the optimal model to test how each predictor variable affected the prediction value. Finally, the optimal model was tested against new data that had not been encountered before to simulate how a production ready model would work in the real world.