<p>Stroke remains a significant global health concern, necessitating accurate and timely prognosis to optimize patient care and outcomes. In recent years, deep learning, a subset of artificial intelligence, has emerged as a promising tool for enhancing stroke prognosis by leveraging its capability to analyze complex clinical and imaging data. This advancement has sparked a significant increase in research publications in this domain. Therefore, our objective in this systematic literature review (SLR) is to: systematically review and analyze the existing body of literature to identify key deep learning architectures, evaluate their performance against conventional prognosis methods, explore the range of clinical and neuroimaging data sources employed, and investigate the potential impact of deep learning on personalized stroke management. Our findings reveal that deep learning holds considerable promise in improving stroke prognosis accuracy, offering opportunities for more precise clinical decision-making. However, challenges related to data heterogeneity, interpretability, and clinical integration persist. We discuss these challenges and propose future directions to facilitate the successful integration of deep learning into routine stroke care. As the demand for precise stroke prognosis intensifies, this review serves as a valuable resource for researchers, clinicians, and policymakers alike, offering insights into the current state of deep learning applications in stroke prognosis and guiding efforts toward leveraging artificial intelligence to alleviate the burden of stroke on individuals and healthcare systems.</p>