This survey provides a comprehensive overview of deep learning-based methods for multivariate time series imputation (MTSI), addressing the significant challenge of missing data in various real-world applications. The authors propose a novel taxonomy that categorizes existing methods based on imputation uncertainty and neural network architecture. They also highlight the PyPOTS Ecosystem, an integrated and standardized platform for MTSI research, and discuss key challenges and future directions. The survey covers predictive and generative methods, including RNNs, CNNs, GNNs, attention mechanisms, VAEs, GANs, and diffusion models. Each method's strengths and limitations are discussed, along with their applications and performance in handling different missingness patterns. The paper concludes by identifying areas for further research, such as handling non-ignorable missing data mechanisms, integrating imputation with downstream tasks, and improving scalability.This survey provides a comprehensive overview of deep learning-based methods for multivariate time series imputation (MTSI), addressing the significant challenge of missing data in various real-world applications. The authors propose a novel taxonomy that categorizes existing methods based on imputation uncertainty and neural network architecture. They also highlight the PyPOTS Ecosystem, an integrated and standardized platform for MTSI research, and discuss key challenges and future directions. The survey covers predictive and generative methods, including RNNs, CNNs, GNNs, attention mechanisms, VAEs, GANs, and diffusion models. Each method's strengths and limitations are discussed, along with their applications and performance in handling different missingness patterns. The paper concludes by identifying areas for further research, such as handling non-ignorable missing data mechanisms, integrating imputation with downstream tasks, and improving scalability.