Anemia affects over 1.6 billion people globally, representing a significant public health challenge, particularly in low- and middle-income countries where traditional diagnostic methods face barriers including invasive procedures, skilled personnel requirements, and inadequate laboratory infrastructure. Artificial intelligence (AI) has emerged as a promising technology offering non-invasive, rapid, and cost-effective solutions for anemia detection and management. This narrative review synthesises current literature on AI applications in anemia screening, diagnosis, and clinical management, examining methodologies, performance metrics, implementation challenges, and future research directions. We conducted a comprehensive narrative synthesis informed by systematic search principles, searching PubMed, IEEE Xplore, Scopus, and Web of Science databases with additional hand-searching and expert consultation. AI models demonstrate variable accuracy in anemia detection across diverse data sources, with performance ranging from 75–97 % AUC depending on methodology and validation approaches. Machine learning algorithms such as support vector machines, convolutional neural networks, and random forests show potential for achieving performance comparable to standard blood tests in controlled research settings. Smartphone-integrated applications and point-of-care systems show particular promise for resource-limited settings, though real-world validation remains limited. While AI shows significant potential for enhancing accessibility and diagnostic efficiency in anemia care, critical challenges including data standardisation, algorithmic bias, regulatory compliance, clinical validation in diverse populations, and deployment equity in low- and middle-income countries require urgent attention to ensure equitable implementation and clinical adoption.