Type 1 diabetes is a lifelong metabolic disorder that demands prompt and accurate diagnosis to prevent serious health complications. This study examines the use of machine learning and deep learning methods to enhance the precision of Type 1 diabetes detection. The “Diabetes Binary Health” dataset sourced from Kaggle served as the foundation for this analysis. Feature selection was conducted using a Random Forest classifier, narrowing down the original 22 features to the 15 most impactful ones. Five models—AdaBoost, Random Forest, Neural Networks, Gradient Boosting, and k-Nearest Neighbors—were developed and systematically evaluated. Among them, the AdaBoost model demonstrated superior performance, achieving an AUC of 0.998 and an accuracy rate of 98.4%. These findings highlight the promising role of computational techniques in improving early diagnosis, ultimately supporting better patient management and contributing to the advancement of healthcare strategies for Type 1 diabetes.