This paper provides a comprehensive survey of Personalized Content Synthesis (PCS) using diffusion models, focusing on recent advancements and challenges. PCS aims to generate images that align with specific user-defined prompts using a small set of reference images. The survey categorizes PCS methods into optimization-based and learning-based approaches, discussing their strengths, limitations, and key techniques. Specialized tasks such as personalized object generation, face synthesis, and style personalization are explored, highlighting unique challenges and innovations. The paper also addresses issues like overfitting and the trade-off between subject fidelity and text alignment, proposing future directions for improvement. The contributions include a detailed analysis of diffusion models, a comparison of classical methods, and an overview of specialized tasks, aiming to inspire further research and practical applications in PCS.This paper provides a comprehensive survey of Personalized Content Synthesis (PCS) using diffusion models, focusing on recent advancements and challenges. PCS aims to generate images that align with specific user-defined prompts using a small set of reference images. The survey categorizes PCS methods into optimization-based and learning-based approaches, discussing their strengths, limitations, and key techniques. Specialized tasks such as personalized object generation, face synthesis, and style personalization are explored, highlighting unique challenges and innovations. The paper also addresses issues like overfitting and the trade-off between subject fidelity and text alignment, proposing future directions for improvement. The contributions include a detailed analysis of diffusion models, a comparison of classical methods, and an overview of specialized tasks, aiming to inspire further research and practical applications in PCS.