Type 1 Diabetes is a chronic autoimmune condition in which insulin-producing beta cells in the pancreas are destroyed, resulting in limited insulin production. Insulin deficiency causes glucose to accumulate in the blood and leads to serious health problems. Thus, people with Type 1 Diabetes often require daily synthetic insulin. In diabetes management, continuous glucose monitoring is an important feature that provides near real-time blood glucose data, which is useful in controlling synthetic insulin dosage. To improve diabetes management systems, we experimented with various machine learning methods, such as deep neural networks, deep reinforcement learning, and ensemble regressors, to predict blood glucose levels at a 30-minute prediction horizon. This enables us to anticipate glycemic fluctuations and offer proactive insulin adjustments to maintain normoglycemia. We assessed our models using several evaluation metrics and demonstrated their performance across different glycemic conditions. The source codes for this work can be found in: https://github.com/soon-jynn-chu/t1d_bg_prediction