Data mining methods have been proposed for finding hidden information in databases. When data is massive, dispersed, and heterogeneous, data mining and knowledge extraction become difficult. Classification is a common prediction task in data mining. Countless AI calculations have been proposed for the reason. Group learning consolidates numerous base classifiers to work on the exhibition of individual order calculations. In particular, distributed data mining relies heavily on ensemble learning. In this way, investigation of gathering learning is vital to apply it in true information mining issues. Ensemble learning is a well-established technique in machine learning that involves combining the predictions of multiple models to improve overall accuracy. In the context of distributed data mining, where data is spread across multiple locations or nodes, ensemble learning becomes more challenging. However, a novel approach to ensemble learning in distributed data mining has been proposed that addresses this challenge. In this paper, we propose a way to deal with build group of classifiers and review its presentation utilizing famous learning calculations on an assortment of freely accessible datasets from biomedical space.