Background: This study presents a machine learning–augmented electrochemical biosensing platform designed to enhance the precision and stability of glucose monitoring in artificial sweat. Conventional amperometric sensors are prone to signal drift, interference from electroactive compounds, and fluctuations in pH and temperature, which collectively restrict their long-term applicability in wearable systems. We simulated glucose oxidase-based sensors and obtained a dataset of 2,000 measurements under systematically varied glucose levels, environmental conditions, and interferent exposure. Several computational models were trained to quantify glucose from raw electrochemical signals, with Long Short-Term Memory networks yielding the highest predictive performance (RMSE 0.29 mM; R² 0.97). Relative to standard calibration procedures, the machine learning–enhanced approach markedly reduced interference artefacts and compensated for environmental perturbations, resulting in improved estimation accuracy and substantially greater resistance to temporal degradation. Notably, the integrated system sustained measurement deviations within ±5% over 30 days, whereas traditionally calibrated sensors exhibited pronounced signal deterioration.