The main aim of the project is prediction related to type 1 diabetes. In this condition, it is the immune system itself that attacks and destroys the beta cells in the pancreas that inadvertently produce insulin. While the exact cause of the condition is yet unknown, environmental variables such as genetic predispositions and viral infections are believed to play a significant role. Our study aims to assess the influence of numerous factors, including age, family history, and other relevant characteristics, on the risk of type 1 diabetes. We experimented with several machine learning methods, including random forest (RF), gradient boosting (GB), support vector machines (SVM), and logistic regression (LR), in an effort to determine which prediction model worked best. By comparing their performance in terms of accuracy, precision, and other evaluation characteristics, we were able to establish which approach was more effective for accurately predicting the beginning of type 1 diabetes. The study’s conclusions provide important new insights into key risk factors as well as a reliable machine learning methodology for the early diagnosis and identification of the disease.