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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. This study critically investigates the intricate relationship between various buildings’ characteristics and four distinct energy-related outputs (ie, 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. Using the American Housing Survey dataset, this study highlights the factors impacting energy burden and consumption. It also identifies variables contributing to the disparity in energy burden among these two household groups. The findings enhance our understanding of energy equity issues and equip policymakers with actionable insights to strategize on alleviating energy burden disparities and promoting energy equity across diverse socioeconomic landscapes.