  Precise glucose level monitoring is critical for people with diabetes to
avoid serious complications. While there are several methods for continuous
glucose level monitoring, research on maintenance devices is limited. To
mitigate the gap, we provide a novel neural control system for continuous
glucose monitoring and management that uses differential predictive control.
Our approach, led by a sophisticated neural policy and differentiable modeling,
constantly adjusts insulin supply in real-time, thereby improving glucose level
optimization in the body. This end-to-end method maximizes efficiency,
providing personalized care and improved health outcomes, as confirmed by
empirical evidence. Code and data are available at:
\url{https://github.com/azminewasi/NeuralCGMM}.
