In diabetes, the body produces insufficient or no insulin, does not properly utilize the insulin it produces, or shows a combination of both. As a result of any of these conditions, the body cannot absorb sugar from the blood into the cells, causing high blood sugar levels. Diabetic blood glucose levels are elevated either because of inadequate insulin release (type I diabetes) or because of impeded insulin action (type II diabetes). Health problems such as this can cause physical disability and even death in some cases. Diabetes has affected over 246 million people worldwide as indicated by the World Health Organization (WHO) report, and this number is predicted to ascend to more than 592 million in 2035. Unlike the western world, India has a different type of diabetes - Type I diabetes is relatively rare, while Type II diabetes affects more than 90% of the population. Forecasting and early prediction of Type II diabetes have become increasingly important due to the high incidence of the disease in recent years. An artificial neural network (ANN) is a network of artificial neurons, similar to those found in the human brain, which is used to solve artificial intelligence problems such as image recognition, pattern recognition, classification, prediction, data compression and optimization. Diabetes has been predicted and classified using ANN techniques. The purpose of this study is to identify a technique among various artificial neural network techniques that can accurately predict the blood sugar levels of people with Type II diabetes based on a review of various literature papers. Keywords: Artificial neural network (ANN), Type II Diabetes, Back Propagation Neural (BPN) Networking.