The use of IoT and smart sensors in diabetes has transformed the diagnosis and management of the disease by permitting continuous monitoring of constants, analysis of predictive patterns, and application of differential therapy. Applications of smart and connected things include IoT health devices like CGM and health wearables generate high quality time-series data such as glucose measurements and heart rate measurements, physical activity, and environment. The collected data is processed and analyzed by using various advanced deep learning techniques such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) networks and also, ensemble models. Such models facilitate a diagnosis of the deviation from normal range of glucose levels, future patterns, and advice to users or doctors. With the help of IoT and AI, the presented framework optimizes the diabetes management, makes diagnosis easier, and provides individual approach, which allows decreasing the number of complications and increasing the quality of life of patients. These concepts are discussed in this paper—including the complementary interworking of IoT, smart sensors, and deep learning models for optimizing diabetes management. The prediction accuracy of the proposed system is 92.5%, thereby providing better health monitoring to the user.