Mixture of Experts (MoE) models have emerged as a powerful framework in machine learning, offering a scalable and efficient approach to solving complex tasks by leveraging a mixture of specialized models, or "experts," each of which is responsible for different aspects of the input data. By activating only a small subset of experts at a time, MoE models achieve high performance while maintaining computational efficiency, making them particularly well-suited for large-scale applications. This survey provides an overview of the MoE model, discussing its foundational principles, recent advancements, and current challenges. We examine the role of the gating mechanism, the trade-off between model complexity and computational efficiency, and the challenges of training stability, expert redundancy, and scalability. Additionally, we highlight recent innovations in sparse activation, hybrid architectures, and integration with metalearning and self-supervised learning. Despite the progress made, challenges related to interpretability, fairness, and deployment in real-world systems remain. Finally, we explore future directions for MoE research, including smarter expert selection, multimodal learning, and edge deployment. This survey aims to provide a comprehensive understanding of MoE models and their potential to drive future advancements in artificial intelligence.

Mixture of Experts (MoE) is an advanced machine learning paradigm that enhances model scalability, efficiency, and specialization by dynamically selecting subsets of experts for each input. This survey provides a comprehensive overview of MoE, covering its fundamental principles, advantages, challenges, recent advancements, and future research directions. MoE architectures have been widely adopted in large-scale artificial intelligence systems, particularly in natural language processing and computer vision, due to their ability to optimize computational resources while maintaining high model capacity. Key challenges such as training instability, expert imbalance, computational overhead, and interpretability are discussed alongside potential mitigation strategies. Recent innovations, including hierarchical MoE, improved gating mechanisms, adaptive expert pruning, and MoE integration into multimodal learning, have significantly enhanced the framework's effectiveness. The survey also explores future research directions, focusing on improving training stability, fairness, continual learning, and resource efficiency. As MoE continues to evolve, it remains a promising approach for developing highly efficient and scalable AI systems.