This survey provides a comprehensive overview of deep learning methods for multivariate time series imputation (MTSI). Missing values are common in MTS data, posing challenges for accurate analysis. Deep learning methods leverage complex temporal dependencies and data distributions to handle missing data. The paper proposes a novel taxonomy categorizing methods based on imputation uncertainty and neural network architecture. It summarizes existing MTSI toolkits, highlighting the PyPOTS ecosystem, which offers an integrated framework for MTSI research. Key challenges and future directions are discussed, including missingness patterns, downstream task integration, and model scalability. The survey also reviews predictive and generative imputation methods, including RNN-based, CNN-based, GNN-based, and attention-based models. Generative methods such as VAE-based, GAN-based, and diffusion-based models are analyzed for their ability to generate varied imputations and quantify uncertainty. Large model-based methods, including pre-trained foundation models and large language models, are explored for their capacity to handle complex temporal dependencies and diverse missingness patterns. The paper also discusses existing MTSI toolkits and benchmarking frameworks, emphasizing the importance of scalable and efficient solutions for large-scale missing data. Future research directions include improving scalability, integrating imputation with downstream tasks, and exploring multimodal learning for more accurate imputation. The survey aims to serve as a valuable resource for researchers and practitioners in time series analysis and missing data imputation.This survey provides a comprehensive overview of deep learning methods for multivariate time series imputation (MTSI). Missing values are common in MTS data, posing challenges for accurate analysis. Deep learning methods leverage complex temporal dependencies and data distributions to handle missing data. The paper proposes a novel taxonomy categorizing methods based on imputation uncertainty and neural network architecture. It summarizes existing MTSI toolkits, highlighting the PyPOTS ecosystem, which offers an integrated framework for MTSI research. Key challenges and future directions are discussed, including missingness patterns, downstream task integration, and model scalability. The survey also reviews predictive and generative imputation methods, including RNN-based, CNN-based, GNN-based, and attention-based models. Generative methods such as VAE-based, GAN-based, and diffusion-based models are analyzed for their ability to generate varied imputations and quantify uncertainty. Large model-based methods, including pre-trained foundation models and large language models, are explored for their capacity to handle complex temporal dependencies and diverse missingness patterns. The paper also discusses existing MTSI toolkits and benchmarking frameworks, emphasizing the importance of scalable and efficient solutions for large-scale missing data. Future research directions include improving scalability, integrating imputation with downstream tasks, and exploring multimodal learning for more accurate imputation. The survey aims to serve as a valuable resource for researchers and practitioners in time series analysis and missing data imputation.