Blood glucose is typically measured using invasive methods such as finger pricking, which although accurate are not suitable for frequent use as they cause extreme pain, and do not provide provisions for continuous glucose monitoring. Recent studies have proposed non-invasive glucometers that are based on scientific principles such as optical polarimetry, thermal emission, and electromagnetic approaches, but are expensive, highly sensitive to external noise and environmental variations, have low signal-to-noise ratio (SNR), and poor glucose selectivity. Although developments in Near-Infrared Spectroscopy (NIRS) have overcome these limitations to a certain extent, they do not produce reliable measurements due to large calibration errors that often result in incorrect glucose readings. In this paper, we propose a robust particle-swarm optimization-based artificial neural network for non-invasive continuous glucose monitoring using the principles of NIRS. We show that the PSO-ANN approach outperforms the traditional backpropagation algorithm used in ANN training and several other regression algorithms with the lowest error metrics: MAE- 1.01, MSE-2.16, RMSE-0.97, R-sqaured score -0.976 and modified R-squared score -0.973. The paper also provides insights into the circuit design, sensors used, hardware-software integration, and clinical validation alongside providing an overview of HbA1c computation.The accuracy and reliability of the proposed system are analyzed using the Clarke Error Grid (CEG) with 93.9% of the obtained readings falling within zone A and 100% of the readings falling in the clinically accepted range (zones A and B). The paper also explores potential enhancements such as miniaturization of the prototype device for wearable applications and wireless connectivity.Â