This paper reviews the challenges and solutions for small data in Prognostics and Health Management (PHM). PHM is crucial for enhancing equipment reliability and reducing maintenance costs, and recent advancements in big data and deep learning have significantly improved its effectiveness. However, real-world scenarios often pose complex working conditions and high costs for data collection, leading to small data challenges. The paper defines small data in PHM, analyzes its causes and impacts, and explores mainstream approaches to address these challenges, including data augmentation, transfer learning, and few-shot learning. It also discusses benchmark datasets and experimental paradigms to facilitate fair evaluations of different methodologies. Finally, the paper identifies promising research directions to inspire future work in this field. The review aims to provide a comprehensive overview of the current state and future prospects of small data challenges in PHM, addressing key questions such as the definition, causes, and solutions for small data, and the impact on PHM tasks.This paper reviews the challenges and solutions for small data in Prognostics and Health Management (PHM). PHM is crucial for enhancing equipment reliability and reducing maintenance costs, and recent advancements in big data and deep learning have significantly improved its effectiveness. However, real-world scenarios often pose complex working conditions and high costs for data collection, leading to small data challenges. The paper defines small data in PHM, analyzes its causes and impacts, and explores mainstream approaches to address these challenges, including data augmentation, transfer learning, and few-shot learning. It also discusses benchmark datasets and experimental paradigms to facilitate fair evaluations of different methodologies. Finally, the paper identifies promising research directions to inspire future work in this field. The review aims to provide a comprehensive overview of the current state and future prospects of small data challenges in PHM, addressing key questions such as the definition, causes, and solutions for small data, and the impact on PHM tasks.