When a human body unable to respond to the insulin properly and/or unable to produce the required amount of insulin to regulate glucose, it means that the human body is suffering from Diabetes. Diabetes increases the risk of developing another disease like heart disease, kidney disease, and damage to blood vessels, nerve damage, and blindness. The diagnosis of diabetes using proper analysis of diabetes data is a significant problem. This study develops forecasts of the number of people with diagnosed diabetes, diagnosed CVD (Cardio-Vascular Diseases) and diagnosed Kidney disease in the Pakistan. A Proposed modeling ANN framework is used to generate forecasts by age, race, weight, and sex. The model forecasts the number of individuals in each of three states (diagnosed with diabetes, not diagnosed with diabetes, and death) in each year using inputs of estimated diagnosed diabetes. Individuals with diabetes have increased rates of all forms of cardiovascular (CV) disorders affecting the heart, blood pressure and kidney. To compare rates of cardiovascular events among patients with diabetic and nephropathy who received conventional antihypertensive as well as the cardiovascular diseases. Artificial Neural Networks (ANNs) are usually used as the tools which can help to analyze cause-effect relationships in complex systems. On the other hand, when the health sciences faces so many complexity more than any other scientific discipline, and in this situation, the large datasets are seldom available. In this case, I show how a particular neural network tool, which can be handle the small datasets of experimental or observational data as well as it can help in identifying the main causal factors leading to changes in some variable which summarizes the behavior of a complex system, for instance the onset of a disease. A detailed description of the neural network tool is given and its application to a specific case study is shown. Recommendations for a correct use of this tool are also supplied.