Juvenile diabetes has emerged as a critical global health challenge, affecting millions of children worldwide, with cases steadily rising. Despite advancements in medical science, many children remain unaware of their risk, leading to late diagnoses and preventable complications. The lack of early detection and insufficient awareness about preventive measures are significant contributors to this growing epidemic. To address these issues, we present a predictive model that leverages a combination of advanced machine learning algorithms, including Fully Convolutional Neural Networks (FCNN), XGBoost, and Random Forest, to assess a child’s risk of developing Type 1 diabetes. The model analyses a range of factors, such as age, sex, area of residence, HbA1c levels, height, weight, BMI, duration of disease, presence of other diseases, nutritional adequacy, standardized growth rate in infancy, birth weight, autoantibodies, impaired glucose metabolism, and hereditary predispositions, to predict whether a child is at risk or already diabetic. This multi-algorithm approach enhances prediction accuracy, providing users with a reliable assessment of the child’s health status.