Diabetes, often regarded as a chronic illness, is a condition that occurs due to high blood sugar for a prolonged period of time. The risk of obtaining diabetes can be reduced by precise early prediction and analysing factors such as hereditary involvement and several other factors. Although advanced techniques came into existence, we can observe that the risk of developing diabetes is substantially higher among adults due to modern life. Timely treatment and diagnosis are required to prevent the outbreak and the advancement of diabetes. The lack of robustness in the precise early prediction of diabetes is a rigid task due to the size of the dataset and deficient labelled data. In this literature, we propose an architectural framework for the early prediction of diabetes disease where data pre-processing, outlier detection and avoidance, K-fold cross validation, and distinct predictive machine learning (ML) and deep learning (DL) classifiers (Decision Tree, Logistic Regression, and Neural Network) are appointed. In this literature, the ensembling of various machine learning and deep learning classifiers are used as a method of enhancing diabetes prediction, utilising K-fold cross validation as a validation strategy. The base classifiers are hypertuned using the grid search approach by considering numeric hyperparameters. The experiments conducted in this literature were conducted under similar conditions using the benchmark PIMA Indian Diabetes (PID) dataset. As a substitute for the conventional approach of testing the proposed approach, we have chosen the chronic kidney disease (CKD) dataset from the University of California (UCI) machine learning repository as a comparative study.