Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor’s performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5–8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM⁻¹ cm⁻². Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology.

In this study, the authors demonstrate the fabrication, calibration, and testing of a piezoresistive microcantilever‐based sensor for biomedical microelectromechanical system (BioMEMS) application. To use any sensor in BioMEMS application requires surface modification to capture the targeted biomolecules. The surface alteration comprises self‐assembled monolayer (SAM) formation on gold (Au)/chromium (Cr) thin films. So, the Au/Cr coating is essential for most of the BioMEMS applications. The fabricated sensor uses the piezoresistive technique to capture the targeted biomolecules with the SAM/Au/Cr layer on top of the silicon dioxide layer. The stiffness ( k ) of the cantilever‐based biosensor is a crucial design parameter for the low‐pressure range and also influence the sensitivity of the microelectromechanical system‐based sensor. Based on the calibration data, the average stiffness of the fabricated microcantilever with and without Au/Cr thin film is 141.39 and 70.53 mN/m, respectively, which is well below the maximum preferred range of stiffness for BioMEMS applications. The fabricated sensor is ultra‐sensitive and selective towards Hg ²⁺ ions in the presence of other heavy metal ions (HMIs) and good enough to achieve a lower limit of detection 0.75 ng/ml (3.73 pM/ml).