Missing values are a significant challenge in the application of Artificial Intelligence (AI), particularly in healthcare. Incomplete datasets can lead to biased models and unreliable predictions, making it essential to develop robust methods for data imputation. Traditional approaches, such as replacing missing values with the mean or median, and more advanced statistical methods, have been widely applied. However, recent advancements in Machine Learning (ML) and Deep Learning (DL) offer promising alternatives. In healthcare, time is a crucial factor, especially in Intensive Care Units (ICUs) where timely decisions can impact patient outcomes. Health-related datasets often incorporate time information, which can be leveraged to improve the accuracy of imputation methods.