This review article discusses ensemble learning, a research area that aims to integrate data fusion, data modeling, and data mining into a unified framework. Ensemble learning is particularly useful for handling complex data such as imbalanced, high-dimensional, and noisy data, as traditional machine learning methods often struggle with these challenges. The process involves extracting features through various transformations, using multiple learning algorithms to generate weak predictive results, and then fusing these results through voting schemes to achieve better performance. The article reviews the research progress of mainstream ensemble learning approaches, classifies them based on different characteristics, and presents challenges and potential research directions for each approach. It also introduces the combination of ensemble learning with other machine learning hot topics, such as deep learning and reinforcement learning. Ensemble learning methods, such as bagging, AdaBoost, random forest, and gradient boosting, are discussed in detail. These methods use multiple weak classifiers to generate results, which are then integrated through voting schemes to produce a final result. The article highlights the importance of balancing bias and variance in model performance and notes that ensemble learning is highly extensible, allowing it to be combined with various machine learning models for different tasks, such as classification and clustering. The paper also discusses the main research issues of ensemble classification and clustering ensemble, as illustrated in Figures 2 and 3. It concludes with a summary and discussion of the survey, emphasizing the importance of ensemble learning in the field of data mining and machine learning.This review article discusses ensemble learning, a research area that aims to integrate data fusion, data modeling, and data mining into a unified framework. Ensemble learning is particularly useful for handling complex data such as imbalanced, high-dimensional, and noisy data, as traditional machine learning methods often struggle with these challenges. The process involves extracting features through various transformations, using multiple learning algorithms to generate weak predictive results, and then fusing these results through voting schemes to achieve better performance. The article reviews the research progress of mainstream ensemble learning approaches, classifies them based on different characteristics, and presents challenges and potential research directions for each approach. It also introduces the combination of ensemble learning with other machine learning hot topics, such as deep learning and reinforcement learning. Ensemble learning methods, such as bagging, AdaBoost, random forest, and gradient boosting, are discussed in detail. These methods use multiple weak classifiers to generate results, which are then integrated through voting schemes to produce a final result. The article highlights the importance of balancing bias and variance in model performance and notes that ensemble learning is highly extensible, allowing it to be combined with various machine learning models for different tasks, such as classification and clustering. The paper also discusses the main research issues of ensemble classification and clustering ensemble, as illustrated in Figures 2 and 3. It concludes with a summary and discussion of the survey, emphasizing the importance of ensemble learning in the field of data mining and machine learning.