<p>Artificial intelligence has become a powerful catalyst in addressing the challenge of energy poverty, particularly in emerging economies where reliable and cost-effective energy access remains a significant challenge. This research examines the asymmetric impact of artificial intelligence on energy poverty by employing a ‘Quantile-on-Quantile’ methodology, which captures complex and asymmetric connections across multiple quantiles of the variables. Using annual data from 2005 to 2023 for ten emerging economies—India, Brazil, South Africa, Mexico, Indonesia, Bangladesh, Nigeria, Pakistan, Thailand, and the Philippines—the analysis reveals a strong inverse relationship between artificial intelligence and energy poverty. The findings indicate that higher levels of artificial intelligence integration significantly reduce energy poverty, though variations exist across quantiles and countries. In particular, significant reductions in energy poverty are observed at higher quantiles of artificial intelligence in India, Brazil, South Africa, and Bangladesh. However, mixed effects are observed in Nigeria and Pakistan due to the limited availability of artificial intelligence infrastructure. These findings suggest that artificial intelligence-driven technologies such as smart grids, predictive maintenance, and intelligent energy distribution systems play a crucial role in improving energy access. The study concludes that policymakers must adopt artificial intelligence-tailored energy strategies, prioritizing investments in artificial intelligence applications that target infrastructural gaps and regional disparities. Artificial intelligence should be embedded in national energy frameworks to ensure inclusive, efficient, and equitable energy transitions in energy-poor regions.</p>