This survey paper explores the interplay between diffusion models and representation learning, highlighting their potential in both generative modeling and downstream recognition tasks. Diffusion models, which are self-supervised learning methods, have gained significant attention for their ability to learn both low and high-level features from input data without requiring labeled annotations. The paper provides an overview of the mathematical foundations, popular denoising network architectures, and guidance methods in diffusion models. It also details various approaches that leverage pre-trained diffusion models for representation learning, including frameworks that use learned representations for downstream tasks and methods that enhance diffusion models through advancements in representation and self-supervised learning. The survey identifies key areas of existing concerns and potential exploration, aiming to offer a comprehensive taxonomy of current approaches and derive generalized frameworks. It discusses the benefits and challenges of using diffusion models for representation learning, such as the trade-offs between direct and noise prediction parametrizations, and the impact of different guidance methods. The paper also reviews recent advances in backbone architectures, such as U-Net, Dit, and U-ViT, and their applications in diffusion models. Additionally, the survey covers methods for leveraging intermediate activations from pre-trained diffusion models for downstream tasks, including a general representation extraction framework and knowledge transfer techniques. It highlights the importance of selecting the optimal diffusion timestep and intermediate layer for downstream prediction tasks and discusses the effectiveness of different feature extraction strategies. The paper concludes by identifying future directions, emphasizing the need for further research to understand the architectural and optimization choices that enhance diffusion models' representation learning capabilities. It suggests that diffusion models can increasingly challenge current state-of-the-art methods in representation learning, particularly in the context of self-supervised learning and large, unlabeled datasets.This survey paper explores the interplay between diffusion models and representation learning, highlighting their potential in both generative modeling and downstream recognition tasks. Diffusion models, which are self-supervised learning methods, have gained significant attention for their ability to learn both low and high-level features from input data without requiring labeled annotations. The paper provides an overview of the mathematical foundations, popular denoising network architectures, and guidance methods in diffusion models. It also details various approaches that leverage pre-trained diffusion models for representation learning, including frameworks that use learned representations for downstream tasks and methods that enhance diffusion models through advancements in representation and self-supervised learning. The survey identifies key areas of existing concerns and potential exploration, aiming to offer a comprehensive taxonomy of current approaches and derive generalized frameworks. It discusses the benefits and challenges of using diffusion models for representation learning, such as the trade-offs between direct and noise prediction parametrizations, and the impact of different guidance methods. The paper also reviews recent advances in backbone architectures, such as U-Net, Dit, and U-ViT, and their applications in diffusion models. Additionally, the survey covers methods for leveraging intermediate activations from pre-trained diffusion models for downstream tasks, including a general representation extraction framework and knowledge transfer techniques. It highlights the importance of selecting the optimal diffusion timestep and intermediate layer for downstream prediction tasks and discusses the effectiveness of different feature extraction strategies. The paper concludes by identifying future directions, emphasizing the need for further research to understand the architectural and optimization choices that enhance diffusion models' representation learning capabilities. It suggests that diffusion models can increasingly challenge current state-of-the-art methods in representation learning, particularly in the context of self-supervised learning and large, unlabeled datasets.