The metabolic disease known as diabetes is defined by consistently high blood sugar levels. An increase in hunger, thirst, and frequency of urine are symptoms of hyperglycemia. Ignoring diabetes might result in numerous complications. Acute consequences can include deaths, diabetic ketoacidosis, hyperosmolar hyperglycemia, and others. Diabetes is the leading cause of death and disability among those over the age of 65, affecting 537 million people globally. Being overweight, having high cholesterol, having a family history of the disease, not getting enough exercise, eating poorly, etc., are all potential causes of diabetes. People with diabetes often experience an increase in the frequency and volume of urine output. When it comes to patient care, big data analytics are crucial. Databases used by healthcare organizations are massive. Finding previously unseen patterns and information, drawing conclusions, and making accurate forecasts are all possible outcomes of using big data analytics to examine large datasets. Classification and prediction accuracy are low with the current approach. Our suggested diabetes prediction model takes into account a number of commonly used indicators, such as blood sugar, age, skin thickness, outcome diabetes pedigree function, BMI, pregnancy, and a few extrinsic variables that lead to the development of diabetes. When compared to its predecessor, the new dataset significantly improves classification accuracy. This research study has also included a pipeline model for diabetes prediction to enhance the accuracy of our classifications. Logistic regression, AdaBoost, decision tree classifier, K-neighbor classifier, and random forest classifier are among the algorithms that have been utilized; nevertheless, random forest classifier yields the best accuracy. Random forests are used to present neural network estimates, which are estimates of the relevance of variables. They also offer an improved method for handling cases where data is lacking. To fill in the blanks when values are lacking, the variable is utilized most often in a certain node. If you're looking for an accurate classification method, random forests are your best bet. Additionally, random forests can manage datasets with hundreds of variables. It can automatically balance datasets when one class is less frequent than another. Because it deals with variables swiftly, the method is suitable for difficult assignments. The paradigm for ensemble and hybridization machine learning will be utilized in this study to go deeper into past research. In order to identify all the crucial traits for diabetes prediction, the proposed study has accomplished a more comprehensive comparative analysis between various datasets and their properties. It is possible to find the best and most accurate algorithm for diabetes prediction by comparing several algorithms and combinations of algorithms.