Inherited metabolic disorders are rare genetic disorders that often require lifelong, ideally continuous, monitoring to prevent acute complications and disease progression. Current care of inherited metabolic disorders relies on periodic clinical evaluations and static assessments, which fall short of capturing the rapid variability of metabolic parameters. Advancements in digital health technologies are making noninvasive wearable sensors available to a growing number of users. These wearables may help monitor patients affected by inherited metabolic disorders to continuously monitor key biomarkers (such as glucose, lactate, ketones, and ammonia) along with vital signs, nutrition, heart rhythm and physical activity. Such a novel monitoring approach may proactively identify trigger factors and metabolic instability, enabling personalized preventive medicine. A large amount of dynamic clinical data, enhanced by artificial intelligence and machine learning systems, could also support diagnosis, prognostic stratification and therapeutic strategies. This narrative review aims to describe potential future applications of digital health technologies in inherited metabolic disorders. In particular, we focused on the potential use of wearable sensors and digital tools, and on the capabilities of artificial intelligence for diagnostic, prognostic, therapeutic and research purposes.