<p>Medical big data analysis is crucial to advancing healthcare research and improving patient care. However, missing data in these datasets creates a significant challenge for understanding patient profiles and disease patterns. The absence of critical information caused by factors such as incomplete patient records or unreported variables introduces uncertainties that can compromise the accuracy and reliability of analytical results. Resolving the problem of missing data is therefore paramount to ensuring the efficiency of healthcare analyses and improving the overall quality of medical research. This study examines and reviews different methods of imputing missing data in the context of medical big data. Traditional imputation techniques, advanced statistical approaches, machine learning-based models and deep learning-based models are evaluated with regard to their relevance, scalability and performance. Particular emphasis is placed on the unique challenges presented by medical big data, such as high dimensionality and data heterogeneity, as well as the incorporation of domain-specific knowledge. This study further investigates the role of big data technologies in enabling efficient imputation for large-scale medical datasets and evaluates metrics designed to assess imputation quality. Finally, future directions are discussed with the aim of improving missing data imputation strategies and, by extension, data-driven decision-making in the era of medical big data.</p>