This study investigates the efficacy of four imputation techniques K-Nearest Neighbors (KNN-I), Multilayer Perceptron (MLP-I), Support Vector Machine (SVM-I), and Decision Trees (DT-I) on six single/ensemble classifiers (MLP, KNN, SVM, XGB, RF, BAGGED SVM) across seven distinct medical datasets. The analysis revealed that the best classification performance was achieved using MLP-I, across single and ensemble classifiers. Further, Ensembles enhanced classification accuracy, demonstrating superiority over single classifiers irrespective of the imputation technique used. The findings also underscored the importance of using imputation techniques in optimizing medical data analysis and improving diagnostic accuracy.