<p>Energy poverty creates substantial economic, social, and environmental challenges, particularly in developing regions where access to affordable and sustainable energy remains limited. Despite increasing interest in financial innovation, there is still no consensus on which financing mechanisms are most effective in alleviating energy poverty. This study introduces an AI-driven fuzzy decision support framework that integrates Koch snowflake-based fuzzy sets, fractal geometry, and deep learning to identify the most suitable innovative financial products for mitigating energy poverty. The proposed model employs a Siamese network to determine expert weights by analyzing demographic similarities, applies the simple weight calculation (SIWEC) method for criterion weighting, and ranks alternatives using hybrid fuzzy techniques. The findings indicate that risk management and government support are the most critical criteria for designing effective financial instruments, while sukuk and energy leasing emerge as the optimal financial solutions. This research advances the literature by providing a novel fuzzy modeling approach that enhances uncertainty representation and decision accuracy, thereby offering practical insights for policymakers and investors aiming to promote inclusive and sustainable energy finance.</p>