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The energy burden within low-income households presents a significant societal challenge that necessitates a thorough understanding for developing effective and sustainable policies. However, very few studies analyze and model energy-related factors among low-income individuals. Thus, this study critically investigates the relationship between various buildings' characteristics and four distinct energy-related outputs (i.e., energy burden, annual energy cost, annual energy cost per square footage, and annual energy cost per square footage per person) for both low-income and non-low-income households across the United States, using six machine learning algorithms. Leveraging the American Housing Survey dataset, this study identifies factors impacting energy burden and consumption and highlights variables contributing to the energy burden disparity among these two household groups, compromising …