Multi-label classification is a fundamental task in bioinformatics, where each instance can be associated with multiple labels simultaneously. Recently, ensemble learning has gained popularity to address the complex nature of bioinformatics data. This paper presents a comprehensive performance comparison of popular state-of-the-art ensemble methods in the realm of bioinformatics for Multi-Label Classification. The study assesses the effectiveness of ensemble multi-label classifiers on various datasets, considering their characteristics, such as label dependencies, imbalanced data, and the high dimensionality of the output space. Through extensive experimentation, we analyze the performance of these ensemble methods and their applicability to diverse bioinformatics data scenarios. The findings revealed that none of the methods exhibited unequivocal superiority over the others in effectively addressing the majority of challenges arising from multi-label classification tasks, emphasizing the need for choosing methods based on the specific characteristics of the data.