Digital radiography (DR) remains the most widely performed imaging modality worldwide, forming the backbone of diagnostic workflows in emergency care, outpatient clinics, and screening programs. Despite technological advances, challenges such as image noise at low radiation doses, positioning errors, workflow inefficiencies, and increasing radiologist workload persist. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these limitations. This paper reviews the current applications of AI in digital radiography, including image enhancement, automated positioning assistance, abnormality detection, workflow optimization, and radiation dose reduction. Additionally, emerging trends such as multimodal AI integration, explainable AI, edge computing, federated learning, and ethical AI frameworks are discussed. The evidence suggests that AI-enhanced DR can improve diagnostic accuracy, reduce repeat examinations, optimize radiation dose, and support radiographers and radiologists in high-volume clinical environments. However, challenges related to validation, cost, bias, and regulatory standardization remain. Continued collaboration between clinicians, engineers, and regulatory bodies is essential to ensure the safe and equitable integration of AI into routine radiographic practice.