<p>Acute stroke remains a major cause of death and long-term disability, with outcomes strongly dependent on rapid diagnosis and timely treatment; however, significant gaps in access and care persist. Artificial intelligence (AI) offers promising support for stroke diagnosis, treatment optimization, and workflow efficiency, although challenges related to validation, bias, regulation, and real-world impact remain. This review examines the current and emerging AI applications in acute stroke management, critically highlighting their limitations. Relevant English-language peer-reviewed and grey literature were identified through searches of PubMed, Scopus, Web of Science, and Google Scholar, with an emphasis on recent studies. No restrictions were placed on the publication year to allow the inclusion of foundational studies; however, priority was given to recent literature to ensure the relevance and currency of the evidence. An iterative thematic synthesis approach was used to identify the recurring concepts, trends, and challenges across the selected literature. AI is increasingly reshaping acute stroke care by enhancing diagnostic speed and accuracy, optimizing treatment selection, supporting clinical decision-making, and improving workflow efficiency across pre-hospital, hospital, and rehabilitation settings. Through applications in neuroimaging interpretation, large vessel occlusion detection, prognostication, and AI-powered clinical decision support systems, AI has the potential to reduce treatment delays and improve outcomes. However, challenges related to data quality, generalizability, interpretability, clinician trust, regulatory approval, and ethical concerns, particularly bias and equity, remain significant. Continued validation, transparent design, multidisciplinary collaboration, and careful integration into clinical workflows are essential to fully realize the benefits of AI in the management of stroke.</p>