<![CDATA[Intracranial hemorrhage (ICH) represents a challenging neurological emergency, characterized by bleeding within the cranial vault. Timely intervention in this case is directly linked to reduced mortality and prevention of permanent sequelae. The interpretation of radiological examination for ICH is full of challenges, including anatomical variations, imaging artifacts, and small hemorrhages. Artificial Intelligence (AI), particularly through the application of deep learning (DL) models such as Convolutional Neural Networks (CNNs), has emerged as a transformative adjunctive technology for radiological interpretation. This study reviews the growing field of AI for ICH detection on non-contrast head CT scans. A literature search was conducted in major databases (PubMed, ScienceDirect, SpringerLink, Google Scholar) for English articles from 2016 - 2026 using keywords related to artificial intelligence, deep learning, intracranial hemorrhage, and its variations. The analysis synthesizes performance metrics reported in recent studies, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). This study explores the potential of AI in quantifying hemorrhage volume, precise localization, classification of hemorrhage subtypes, and integration into clinical workflows for triage and decision support. Challenges and considerations that accompany the clinical translation of AI are also defined, including the need for external validation, the nature of complex algorithms, ethical and legal standings, data security, and integration into existing radiology information systems. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. This review explores the role of AI in augmenting the diagnosis and management of intracranial hemorrhage, emphasizing its role as a powerful tool to enhance the clinical acumen of radiologists.]]>
