Diabetes mellitus is a chronic metabolic disorder requiring meticulous blood glucose regulation to minimize both acute complications and long-term vascular damage. Traditional glucose monitoring approaches—such as finger-prick tests and continuous glucose monitoring (CGM)—primarily support reactive interventions, often falling short in enabling proactive management. This study proposes a deep learning-based predictive framework for blood glucose level estimation using historical CGM data. The model’s performance was evaluated using standard metrics including Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) score. Experimental results across multiple patients reveal that the model achieved RMSE values ranging from 19.37 to 28.57, and MAPE values between 7.76 and 13.31. The highest predictive accuracy was observed for Patient 570 (RMSE: 20.38, MAPE: 7.76), while the model struggled with higher variability in Patient 559. These findings demonstrate the model’s potential in delivering personalized, anticipatory glycemic control, thereby supporting more effective diabetes management strategies.