Concerning complicated data types such as imbalanced, high-dimensional, or noisy data, conventional machine learning techniques could find it difficult to reach sufficient performance. This remains true despite notable advancements in knowledge-based discovery. We use machine learning and ensemble learning techniques to examine and forecast trends within this massive volume of data. In the field, ensemble learning has become a potent tactic due to its ability to mix different models or algorithms to improve forecast accuracy and robustness. Each base learner has unique prediction patterns and errors, but together they can improve performance and reduce prediction error. This paper emphasis on the challenges and benefits and a study Ensemble model in different area.