Background/Objectives: Hearing loss affects over 1.57 billion people worldwide, and many who could benefit from hearing aids do not wear them. Poor speech understanding in noise is one of the main complaints, and several artificial intelligence (AI) models have been proposed to address it. This review aims to characterize AI applications in hearing devices and offers a framework for advancing from model development to clinical application. Methods: Following PRISMA-ScR guidelines, PubMed/MEDLINE, Embase, CINAHL, Web of Science, and APA PsycInfo were searched for peer-reviewed original studies published from 2010 to 2025. Data regarding AI task, architecture, input, training method, validation, and latency were collected for each model. Results: In total, 46 studies met the inclusion criteria. Speech enhancement was the most common task (58.7%), and deep neural networks were the most common architecture (34.8%). Training was predominantly offline (93.5%) and supervised (91.3%). Validation relied mainly on database/simulation (71.7%) and normal-hearing listeners (39.1%); only 1 model (2.2%) was validated using hearing-impaired listeners in real-world conditions. Ten models (21.7%) met the latency requirements for hearing aid use. Conclusions: AI has been applied across many hearing device tasks, but most models remain early in development. Clinical translation will require on-device implementation and real-world validation in hearing-impaired listeners.