This paper provides a comprehensive review of deep ensemble learning, which combines the strengths of deep learning and ensemble learning to achieve better generalization performance. Deep learning models have shown superior performance compared to traditional models, and deep ensemble learning integrates the advantages of both approaches. The paper categorizes ensemble models into bagging, boosting, stacking, negative correlation-based, explicit/implicit, homogeneous/heterogeneous, and decision fusion strategies. Applications of deep ensemble models in various domains are discussed, and potential future research directions are outlined. Deep learning models are used for classification, regression, and clustering tasks. Classification involves mapping input features to target labels, while regression deals with continuous decisions. Ensemble learning improves model performance by combining predictions from multiple models, such as through averaging, voting, or stacking. The success of ensemble learning is attributed to statistical, computational, and representational aspects, as well as bias-variance decomposition and strength-correlation. Bagging reduces variance by training multiple models on different subsets of data and combining their predictions. Boosting reduces both bias and variance by sequentially training models to correct errors from previous ones. Stacking uses a meta-model to combine predictions from base models. Negative correlation-based methods encourage diversity among models to learn different aspects of the data. Explicit/implicit ensembles aim to achieve ensemble performance without additional computational cost by sharing weights or using techniques like dropout and stochastic depth. Homogeneous and heterogeneous ensembles use models from the same or different families, respectively. Decision fusion strategies combine model outputs using rules like unweighted averaging or majority voting. The paper also discusses challenges in deep ensemble learning, such as computational costs and overfitting, and proposes solutions like snapshot ensembling and regularization techniques. Overall, deep ensemble learning offers improved performance and generalization by leveraging the strengths of both deep learning and ensemble methods. Future research directions include further exploration of diverse ensemble strategies, optimization of training processes, and application in various domains.This paper provides a comprehensive review of deep ensemble learning, which combines the strengths of deep learning and ensemble learning to achieve better generalization performance. Deep learning models have shown superior performance compared to traditional models, and deep ensemble learning integrates the advantages of both approaches. The paper categorizes ensemble models into bagging, boosting, stacking, negative correlation-based, explicit/implicit, homogeneous/heterogeneous, and decision fusion strategies. Applications of deep ensemble models in various domains are discussed, and potential future research directions are outlined. Deep learning models are used for classification, regression, and clustering tasks. Classification involves mapping input features to target labels, while regression deals with continuous decisions. Ensemble learning improves model performance by combining predictions from multiple models, such as through averaging, voting, or stacking. The success of ensemble learning is attributed to statistical, computational, and representational aspects, as well as bias-variance decomposition and strength-correlation. Bagging reduces variance by training multiple models on different subsets of data and combining their predictions. Boosting reduces both bias and variance by sequentially training models to correct errors from previous ones. Stacking uses a meta-model to combine predictions from base models. Negative correlation-based methods encourage diversity among models to learn different aspects of the data. Explicit/implicit ensembles aim to achieve ensemble performance without additional computational cost by sharing weights or using techniques like dropout and stochastic depth. Homogeneous and heterogeneous ensembles use models from the same or different families, respectively. Decision fusion strategies combine model outputs using rules like unweighted averaging or majority voting. The paper also discusses challenges in deep ensemble learning, such as computational costs and overfitting, and proposes solutions like snapshot ensembling and regularization techniques. Overall, deep ensemble learning offers improved performance and generalization by leveraging the strengths of both deep learning and ensemble methods. Future research directions include further exploration of diverse ensemble strategies, optimization of training processes, and application in various domains.