This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma samples using nickel-based electrochemical sensors and machine learning (ML). This strategy enables the detection of a panel of analytes without being limited by the selectivity of the transducer material and leaving accommodation of interference analysis to ML models. Here, we fabricated a non-enzymatic electrochemical sensor for L-lactic acid detection in complex mixtures and human plasma samples using nickel oxide (NiO) nanoparticle-modified glassy carbon electrodes (GCE). This paper presents a data-driven approach for developing transducers that reduce interference effects using ML with a sufficiently large dataset. The interference trends of uric acid, ascorbic acid, and glucose were measured in the presence of L-lactic acid and the complex data set was analyzed using various ML models. Limit of detections of 2.61 μM, 15.99 μM, 11.34 μM, and 3.27 μM for L-lactic acid, uric acid, glucose, and ascorbic acid were obtained, respectively, in a complex mixture using an artificial neural network-based-regression model. Further, the electrochemical signature was recorded for 10 different human plasma samples and analyzed using developed ML models to validate the sensor performance in real samples. The random forest model performance was tested against the L-lactic acid levels in human plasma samples obtained through conventional colorimetric assays which showed a good prediction performance with coefficient of determination (R