When a person is affected by type 1 diabetes mellitus, the lack of insulin requires to introduce in the body the correct amount of insulin particularly after meals, when blood glucose typically increases. Many algorithms in the literature have been used to predict post-prandial insulin boluses, but they only consider meal carbohydrates (which are the main nutritional factor influencing postprandial glucose response). The aim of this work is to improve the accuracy of the prediction also considering the contribution of other nutritional factors such as, for example, sugars, proteins, lipids and fats. In fact, all these factors are expected to affect the glycemic response in different ways. Based on these considerations, the goal of this study is to develop an artificial intelligence algorithm to predict post-prandial glycemia levels taking into account not only nutritional factors but also glycemia before the meal and infused insulin.