The ongoing COVID-19 pandemic has underscored the need for effective predictive tools to manage patient outcomes and healthcare resources. Electronic health records (EHRs), containing a wealth of patient information, have become a vital resource for predicting COVID-19 outcomes. Deep learning, a subset of machine learning, has shown significant promise in extracting patterns from complex healthcare data to predict patient severity, mortality, and recovery. This paper provides a comprehensive review of recent research exploring the integration of deep learning models with EHR data to predict COVID-19 outcomes. It evaluates various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, applied to diverse datasets from patient demographics, clinical histories, laboratory results, and even imaging data. The paper also discusses the challenges faced in this area, such as data quality issues, model transparency, and the integration of predictions into clinical workflows. Finally, the paper offers a perspective on the future directions for improving the use of deep learning models in predicting outcomes, emphasizing the importance of interdisciplinary approaches and addressing ethical concerns such as data privacy and informed consent.