To address the limitations of current glucose monitoring devices—such as inaccuracies and the burden of frequent measurements—this study introduces a cutting-edge deep learning approach using Long Short-Term Memory (LSTM) networks to predict blood glucose levels. By harnessing 13 days of historical blood glucose data, the model is capable of accurately forecasting glucose levels for the subsequent 13 days. This powerful predictive capability reduces the need for continuous glucose monitoring, minimizes the frequency of invasive tests, and significantly enhances patient comfort, all while ensuring effective glycemic control. The proposed LSTM model demonstrates remarkable potential in tracking glucose fluctuations, positioning it as a game-changing tool for diabetes management. Impressively, the model achieves an average prediction error of just 0.0007 when compared to invasive measurements, underscoring its extraordinary accuracy. This breakthrough represents a significant leap forward in non-invasive blood glucose detection, particularly when coupled with near-infrared (NIR) technology, offering unprecedented reliability and convenience for both patients and healthcare providers.