Machine learning and ensemble learning models has extended a substantial consideration from the scientific communal. Ensemble learning models are used to increase the performance of a model by combining multiple classifiers at every iteration with selection of training data. Ensemble learning methods are form strong classifiers by combining weak classifiers in order to increase the concert of classifiers or model. The main motivation of ensemble learning is to correctly combine weak models to obtain an accurate robust model with bias-variance trade off. Machine learning plays vital role in health sector which includes disease prediction, medical images analysis. A drug-side effect or drug adversarial reaction is typically viewed as an adverse subordinate result which arises in accumulation to the anticipated healing consequence of a remedy or pill. Drug side-effects have increased attention from the society because of significant illnesses and humanities they caused. Therefore, the early detection of drug side- effects is one of challenging task. This paper targets the Chronic Kidney Disease (CKD) issue and drug side effect prediction, CKD is one of the major health problems in different states across India. The foremost detached of this work is to provide the robust classification using machine learning and ensemble learning methods based on CKD dataset and drugs dataset to explore meaningful insights of data and also predict the CKD status and drug side effect prediction using various classifiers.