Wearable technologies are rapidly expanding, creating a demand for the real-time monitoring of molecular biomarkers. Non-invasive samples, such as sweat and saliva, are particularly promising for this purpose. However, achieving selectivity and specificity in sensor measurements remains a challenge due to the complexity of biomarkers and the stability of captured molecules. Laser-Scribed (LS) electrodes, fabricated using a CO₂ laser cutter on polyimide substrates, offer a cost-effective and promising alternative for wearable electrochemical sensors and biosensors. This study investigates the optimization of LS electrode manufacturing parameters using a 60 W CO₂ laser cutter and explores their application for the label-free detection and classification of biomarkers in sweat. Cyclic voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) experiments were conducted to characterize the electrochemical performance of LS electrodes, with a focus on detecting L-histidine in artificial sweat. The optimized LS electrodes exhibited high sensitivity, demonstrating a linear relationship (R² = 0.987) between the current peak and L-histidine concentration in the range from 8.3 mM to 50 mM. Additionally, an MLPNN (Multilayer Perceptron Neural Network) machine learning algorithm was trained using CV data to classify L-histidine in artificial sweat for lower, physiologically relevant concentrations (e.g., 0.12 – 3.3 mM) where linearity is lost. The results achieved 90% accuracy, highlighting the potential of LS electrodes for real-time, label-free biomarker monitoring in wearable health devices. In conclusion, this study demonstrates the effectiveness of LS electrodes and data-driven classification techniques for sweat component monitoring. Future research will focus on improving the detection capabilities of LS electrodes and expanding their application to classify other sweat biomarkers, such as NaH₂PO₄, NaCl, and Na₂HPO₄. This work advances the development of high-performance and disposable wearable biosensors for non-invasive health monitoring.