Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning, details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Note to Practitioners—This work explores the intersection of IL and dexterous manipulation. With promising applications in manufacturing, healthcare, and home robotics, IL-based approaches allow robots to handle delicate objects, execute precise tasks, and operate in diverse, unstructured environments. However, key challenges remain—particularly in collecting high-quality demonstrations and enabling generalization from limited data, both of which are critical for real-world deployment. This survey aims to serve as a practical and accessible guide for practitioners seeking to apply IL-based methods to develop more capable and adaptable robotic systems in real-world scenarios.