Artificial intelligence (AI) and more specific subsets of AI such as machine learning (ML) and deep learning (DL) have become widely available in recent years. Open source software packages and languages have made it possible to implement complex AI based data analysis and modeling techniques on a wide range of applications. The application of these techniques can expedite existing models or reduce the amount of physical testing required. Two data sets were utilized to examine the effectiveness of multiple ML techniques to estimate experimental outcomes and to serve as a substitute for additional testing. To achieve this complex multi-variant regressions and neural networks were utilized to create estimating models. The first data sets of interest consist of a pool fire experiment that measured the flame spread rate as a function of initial fuel temperature for 8 different fuels, including Jet-A, JP-5, JP-8, HEFA-50, and FT-PK. The second data set consists of hot surface ignition data for 9 fuels including 4 alternative piston engine fuels for which properties were not available. When properties were not available multiple imputation by chained equations (MICE) was utilized to estimate fluid properties. 10 different ML techniques were implemented to analyze the data and R-squared values as high as 92% were achieved. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.