The prevalence of diabetes is increasing globally, necessitating efficient methods to enhance the timely identification and treatment of diabetes. Focusing on early detection and effective management strategies for complications is critical. In this study, an integrated solution comprising two modules is presented: diabetic detection and diabetic retinopathy detection. The diabetic detection module employs machine learning techniques such as decision trees, random forests, and KNN to forecast the presence of diabetes based on patient data. The diabetic retinopathy detection module utilizes deep learning techniques, specifically the ResNet50 model architecture, to analyze retinal images and identify signs of diabetic retinopathy. A comprehensive implementation of both modules, including data preprocessing, model training, and evaluation, using Python libraries such as TensorFlow, Keras, and scikit-learn is provided. The trained models are integrated into a web application. This web application allows users to input their medical data and retinal images and receive real-time predictions regarding their diabetic status and risk of diabetic retinopathy. The integration of these modules into a web application provides an intuitive interface tailored for both healthcare professionals and patients to assess diabetic risks conveniently. Furthermore, it facilitates early intervention and management of diabetic complications, ultimately improving patient outcomes and reducing healthcare burdens.