This survey provides an in-depth overview of the Lottery Ticket Hypothesis (LTH), which posits that a dense neural network contains a sparse subnetwork (winning ticket) that can achieve performance comparable to the original model when trained in isolation. The survey discusses the theoretical foundations, experimental insights, and practical applications of LTH, highlighting its relevance to various neural network architectures, including Transformers, GNNs, and generative models. It also addresses challenges such as efficiency, scalability, and the need for standardized experimental settings. The survey categorizes existing research into eight topics: theory, special models, experimental insight, algorithms, efficiency, relations with other topics, open issues of existing works, and applications. It reviews efficient strategies for iterative pruning, discusses the connection between LTH and other domains like robustness and federated learning, and presents a benchmark for future research. The survey also identifies key factors influencing the success of LTH, such as initialization, signs, and supermasks. It explores various algorithms for pruning, including initialization and unstructured pruning, and discusses the efficiency of LTH in terms of computational cost and resource usage. The survey highlights the potential of reusing winning tickets across different tasks and models, and addresses challenges in applying LTH to lifelong learning and multi-task learning scenarios. Overall, the survey aims to provide a comprehensive understanding of LTH and serve as a valuable resource for researchers and practitioners in the field.This survey provides an in-depth overview of the Lottery Ticket Hypothesis (LTH), which posits that a dense neural network contains a sparse subnetwork (winning ticket) that can achieve performance comparable to the original model when trained in isolation. The survey discusses the theoretical foundations, experimental insights, and practical applications of LTH, highlighting its relevance to various neural network architectures, including Transformers, GNNs, and generative models. It also addresses challenges such as efficiency, scalability, and the need for standardized experimental settings. The survey categorizes existing research into eight topics: theory, special models, experimental insight, algorithms, efficiency, relations with other topics, open issues of existing works, and applications. It reviews efficient strategies for iterative pruning, discusses the connection between LTH and other domains like robustness and federated learning, and presents a benchmark for future research. The survey also identifies key factors influencing the success of LTH, such as initialization, signs, and supermasks. It explores various algorithms for pruning, including initialization and unstructured pruning, and discusses the efficiency of LTH in terms of computational cost and resource usage. The survey highlights the potential of reusing winning tickets across different tasks and models, and addresses challenges in applying LTH to lifelong learning and multi-task learning scenarios. Overall, the survey aims to provide a comprehensive understanding of LTH and serve as a valuable resource for researchers and practitioners in the field.