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Energy justice advocates for the equitable and accessible provision of energy services, mainly focusing on marginalized communities. Adopting machine learning in analyzing energy-related data can unintentionally reinforce social inequalities. This perspective highlights the stages in the machine learning process where biases may emerge, from data collection and model development to deployment. Each phase presents distinct challenges and consequences, ultimately influencing the fairness and accuracy of machine learning models. The ramifications of machine learning bias within the energy sector are profound, encompassing issues such as inequalities, the perpetuation of negative feedback loops, privacy concerns regarding, and economic impacts arising from energy burden and energy poverty. Recognizing and rectifying these biases is imperative for leveraging technology to advance society rather than perpetuating existing injustices. Addressing biases at the intersection of energy justice and machine learning requires a comprehensive approach, acknowledging the interconnectedness of social, economic, and technological factors.