Diabetic retinopathy (DR) is a major complication of diabetes and a leading cause of vision loss worldwide. Beyond its ocular effects, the severity of DR can signal broader systemic issues, often correlating with an increased risk of complications that may result in hospitalization. Early identification of patients at high risk for hospitalization through retinal images offers a non-invasive, cost-effective method for proactive healthcare management. This project introduces a novel approach combining deep learning and Monte Carlo methods to predict hospitalization risks based on retinal images of diabetic retinopathy. In this approach, Convolutional Neural Networks (CNNs) are used to extract and classify features from retinal images, focusing on key indicators of DR severity such as microaneurysms, hemorrhages, and exudates. The images are preprocessed and segmented before being fed into the CNN model. Monte Carlo simulations are applied to the deep learning model to quantify prediction uncertainty, offering insights into the reliability of the risk assessments. The combination of deep learning and Monte Carlo methods improves prediction accuracy and sensitivity, providing a more comprehensive assessment of risk. This integrated approach allows healthcare providers to identify high-risk patients earlier, enabling tailored interventions that could reduce hospitalization rates and prevent serious complications. Additionally, the Monte Carlo method’s probabilistic nature helps clinicians better understand the uncertainty in the predictions, supporting more informed decision-making. Future work will focus on expanding the dataset, refining the model, and exploring its real-time clinical applications.