Ischemic stroke is a cerebrovascular condition characterized by a high rate of illness and death, presenting a significant challenge to human health and survival. At present, the diagnosis and management of ischemic stroke heavily rely on the visual examination of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, the accuracy of these diagnostic methods is influenced by equipment-related artifacts, noise, and the experience of the radiologist. To address these limitations, there has been a notable increase in the development of computer-aided diagnostic (CAD) techniques for ischemic stroke over the past decade. Particularly, deep learning models, with their ability to process extensive amounts of data, are acknowledged as valuable adjunct tools for acute intervention and prognosis guidance in ischemic stroke. This review aims to provide an overview of the current cutting-edge deep learning technology, followed by a summary of its key applications in acute ischemic stroke imaging, with a focus on its potential role in stroke diagnosis and multimodal prognostication. Lastly, the current challenges and future prospects in this field will be outlined.