Entity Linking (EL) plays a crucial role in mapping textual mentions to corresponding entities in structured knowledge bases, while Multi-modal Entity Linking (MEL) extends this task by incorporating both textual and visual information. A key challenge in MEL is effectively utilizing multi-modal contextual data to improve entity disambiguation, particularly when candidate entities are highly similar. In this paper, we propose a novel approach that leverages the cross-modal attention and reasoning capabilities of multi-modal large language models (MLLM) to enhance MEL in an unsupervised setting. Our model consists of an optimized re-ranking stage that reduces computational cost by narrowing down the number of candidate entities, and a comparative selection stage that improves entity mention identification accuracy by fully leveraging indirect and latent information to distinguish highly similar candidates. Experimental results on three benchmark datasets show significant improvements in top-1 accuracy, with our model achieving \(94.83\%\) , \(94.19\%\) , and \(91.25\%\) on the Wiki-MEL, Richpedia-MEL, and WikiDiverse datasets, respectively, surpassing state-of-the-art approaches by up to \(4.95\%\) or more.