Type-1 Diabetes (T1D) is a chronic autoimmune disease affecting millions of patients worldwide. The pancreas of T1D patients no longer produces the insulin responsible for regulating blood glucose levels. Therefore, T1D patients must inject insulin themselves either through multiple daily injections or continuous subcutaneous insulin infusion (insulin pumps). They must daily manage their diabetes to reach the advised blood glucose targets and avoid short-term and long-term complications. With the technological advances achieved in the last decades in diabetes- and non-diabetes-oriented technologies, a large amount of data has become available. This has encouraged researchers to apply artificial intelligence techniques to improve diabetes management by predicting blood glucose levels or recommending actions for better control of T1D. Our paper reviews recent contributions in this area focusing on data-driven approaches for blood glucose prediction. It highlights future research avenues to better exploit the new advances in machine learning and deep learning models.