Diabetes Mellitus (DM) is a chronic metabolic disorder that poses a significant global health burden, affecting millions worldwide. Early detection and prediction of diabetes are critical for timely intervention and improved patient outcomes. The increasing availability of Electronic Health Records (EHRs) has provided a valuable data source for leveraging deep learning techniques to enhance diabetes prediction. This study explores the application of deep learning models in predicting diabetes using structured and unstructured EHR data. This review various deep learning architectures, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) for medical imaging, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential health data analysis, and Autoencoders for feature extraction and anomaly detection. The performance of these models is evaluated using metrics such as accuracy, precision, recall, F1-score, and the Area under the Receiver Operating Characteristic Curve (AUC-ROC). Additionally, we discuss data preprocessing techniques, including handling missing values, normalization, and feature selection, which are crucial for optimizing model performance. Despite the promising advancements, challenges such as data privacy, class imbalance, model interpretability, and generalizability across diverse populations remain significant barriers to widespread clinical adoption. We highlight emerging trends such as federated learning for privacy-preserving AI, the integration of wearable health devices for real-time monitoring, and explainable AI (XAI) to enhance trust and transparency in deep learning-based diabetes prediction. This underscores the potential of deep learning in transforming diabetes prediction and management by providing accurate, scalable, and automated diagnostic tools. Future research should focus on improving model robustness, enhancing interpretability, and integrating deep learning models into real-world clinical workflows for effective diabetes prevention and personalized healthcare interventions.