Losses of data are a widespread issue of the real-world tabular data, utilized in machine learning (ML). Missing values may dramatically hamper the quality of the model, be biased, and result in incorrect inferences unless addressed correctly. This is a systematic literature review (SLR) that explores and syntheses 52 research articles published 2020-2026 in high-impact peer review journals. The review is done under the guidelines of PRISMA (Preferred Reporting Items to Systematic Reviews and Meta-Analyses). Methods of imputation can be divided into 5 broad categories: statistical and conventional imputation methods (mean, median, mode, and Last Observation Carried Forward), machine learning-based methods (k-Nearest Neighbors, Random Forest, Decision Trees and Support Vector Machines), multiple imputation methods (including MICE, missForest and missRanger), deep learning-based methods (including Autoencoders, Vari There is a systematic comparison between methods based on type of dataset, missing data mechanism (MCAR, MAR, MNAR), evaluation measures (RMSE, MAE, accuracy, AUC), computational complexity and scalability. Using our results, it appears that, up to low missingness rates, conventional approaches are equally competitive, but that deep generative models (with GAN-based models or diffusion-based models being two different approaches to the same task) are matched when applied to high-dimensional and heterogeneous tabular data. However, there is no one particular approach that prevails in all situations. This review finds the overall gaps in research, such as the absence of standardized benchmarks, the relative dearth of interest in MNAR mechanisms, and the lack of research on imputation in federated learning. The results give practical advice to practitioners and researchers to use the right imputation techniques when using tabular ML tasks.