Clustering approaches in mathematical statistics and machine learning improve prediction performance by using multiple learning algorithms. Ensemble learning involves a modular collection of models, such as classification algorithms or experts, strategically coupled to tackle computational intelligence challenges. Generally, ensemble learning is used to conduct and enhance the accuracy of models (classification, prediction, function approximation, and so on) or to limit the chance of unintentional bad selection. In addition to offering a level of confidence in the model's choice, selecting optimal (or near-optimal) features or qualities, data consolidation, incremental learning, non-static learning, and error correction are all uses of ensemble learning. This page explains ensemble learning, its several varieties, fields of application, studies and research that have employed this technology in learning, and the correctness of the results.