Background- The human ear offers a unique, stable and accessible anatomical structure for biometric identification. While anthropological methods provide population-specific insights, they are limited by manual effort and scalability. Digital ear biometrics, using AI and imaging, allows fast, accurate and real time recognition. This review compares both approaches, highlighting their strengths, limitations and the emerging potential of hybrid systems to revolutionize biometric identification across forensic, clinical and security settings. Methodology- A systematic review was conducted following PRISMA guidelines. Databases such as PubMed, Scopus, Google Scholar and ScienceDirect were searched using keywords like ear biometrics, ear recognition, anthropometry and digital identification. From 1000 records initially screened, 31 studies (1980-2024) met the inclusion criteria based on relevance, methodological rigor and measurable biometric outcomes. Studies involving non-human samples, lacking ear-based identifiers, or unrelated to biometric recognition were excluded. Each study was evaluated using the Cochrane Risk of Bias tool. The analysis included both anthropometric-based ear studies focusing on morphological features and digital methods utilizing 2D/3D imaging, CNNs and AI-integrated models like Edge Ear.