Favipiravir (FPV) is a widely used drug that works well against a variety of infectious RNA viruses. It is commonly used as an anti-influenza medication. To treat patients infected with RNA viruses, combination therapy consisting of FPV and levodropropizine (LDP) is required. As a result, an effective electrochemical sensor for identifying FPV in human plasma samples has been developed. The hydrothermal method is used to synthesize NiFe2O4NPs, a binary transition metal oxide comprising iron and nickel. The effective synthesis of these materials is verified by energy-dispersive X-ray analysis (EDX) and X-ray diffraction (XRD). The developed nanocomposite underwent physicochemical and electrochemical characterization, and the experimental setup was refined. Two broad linear dynamic ranges were obtained for both medications using Differential Pulse Voltammetry (DPV): 0.5 µM–200 µM for FPV and 1.0 µM–110 µM for LDP. For FPV, the computed detection and quantitation limits were 0.148 µM and 0.45 µM, while for LDP, they were 0.297 µM and 0.9 µM. The recommended technique was successfully used to analyze both medications at the same time in human plasma samples, and acceptable percentage recoveries were attained. In terms of greenness, EcoScale and GAPI greenness tools were used to evaluate the analytical methodology. Additional studies on repeatability, stability testing, interference analysis, and greenness evaluation supported the validity of the recommended method for the simultaneous measurement of FPV and LDP in laboratories for quality assurance. Additionally, Lean Six Sigma (LSS) quality technique is regarded as a collection of management disciplines for continuous improvement and method control. It is thought to offer the most practical way to address the negative effects of variation by taking steps toward analytical process improvement. As a result, LSS can be considered an invaluable tool in any laboratory, serving as a standard method for process optimization and problem solutions as well as a managerial discipline. Furthermore, it guarantees a strong method specification at a high degree of statistical confidence and targeted performance.