Abstract: Continuous glucose monitoring is important in the management of diabetes. According to statistics, diabetes is the third chronic non-infectious disease that seriously endangers people's health, followed by tumor as well as cardiovascular and cerebrovascular diseases. In 2019, globally, there were a total of 460 million diabetics aged 20–79 years, which accounted for 9.1% of the total population in this cohort. Each figure is projected to increase to 592 million and by 10.1% respectively by 2035. Currently, the methods of blood glucose monitoring can be divided into invasive, minimally invasive, and noninvasive. The main methods for blood glucose monitoring include irregular sampling of fingertip blood or consecutive measurement of interstitial fluid glucose based on implantable sensors. However, these methods have some limitations, which include pain sensation, high cost, short service life, and susceptibility. Patients need to measure their blood glucose frequently. Invasive and minimally invasive monitoring will cause physical and psychological pain. Therefore, noninvasive monitoring is one of the most promising techniques for continuous monitoring of blood glucose, and it has a broad market prospect. In this study, the electrocardiogram (ECG signals) were used to achieve the noninvasive monitoring of blood glucose levels. First, 756160 ECG periodic signals of 12 volunteers for 60 d were obtained from the experiment. Second, the ECG signals were preprocessed using an infinite impulse response filter. Furthermore, a method combining convolutional neural networks and long short-term memory networks (CNN-LSTM) was proposed for blood glucose monitoring. In Addition, two modeling methods (individual modeling and group modeling) were investigated in this study. The results show that the precision of blood glucose monitoring under the condition of individual and group modeling is 80% and 88%, respectively. The F1-score of the group modeling can reach 0.95, 0.88, 0.91, 0.85, 0.92, 0.88, 0.86, 0.86, 0.87, and 0.86. Therefore, this study indicates that the proposed method based on ECG signals can provide powerful theoretical support and technical guidance for real-time and accurate blood glucose monitoring.