This paper presents four modeling techniques for predicting household transportation energy consumption by exploring decision trees, random forest, and neural networks in addition to elastic net regularization analyses. The main objective of this study is to evaluate how effectively these advanced statistical models can be applicable to a Transportation Module (TM) operating within the Integrated Urban Metabolism Analysis Tool (IUMAT), a system-based computational platform for urban sustainability evaluation. The Delaware Valley Regional Planning Commission (DVRPC) travel demand model is used to estimate household transportation energy use based on household trip demand generation, travel mode, fuel type, distance and duration. The Household Travel Survey (HTS) and Traffic Analysis Zones (TAZ) drawn from the DVRPC database are used for model training. Our results indicate that machine learning algorithms, thanks to their ability to accommodate non-linearity, have significantly higher accuracy in predicting household transportation demand. We show that the Neural Network (NN) model out-performs the decision tree model, predicting transportation energy demand resulting in lower Mean Squared Error and a higher R². Using a Random Forest analysis for individual variable impact testing, we also demonstrate that the number of households' motorized trips and the travel distance are the most significant predictors of household transportation energy consumption.