The demand for monoclonal antibodies (mAbs) in hospitals is continually rising due to the advancement of targeted therapy or immunotherapy. Typically, mAbs are produced in centralized chemotherapy production units and require rigorous control to ensure the right drug at the right dose before administration to patients. This study aimed to develop a robust discriminant analysis method using a large dataset comprising 22 mAbs used in cancer therapy. The discriminant analysis was based on UV spectroscopy coupled with chemometrics. A dataset consisting of 404 samples was prepared and analyzed using various preprocessing methods, wavelength selection and discriminant analysis algorithms to maximize overall accuracy. Validation of the newly developed discriminant analysis was conducted using samples collected from real manufactured preparations for patient (over 1,380 samples). The study achieved an overall accuracy of 99.6% and 99.8% on the 22 mAbs using partial least squares-discriminant analysis (PLS-DA) and k-nearest neighbors (kNN), respectively. The utilization of chemometrics for discriminating mAbs based on their UV spectra, following total area normalization, has been successfully demonstrated. This method has undergone validation, affirming its capability to swiftly and dependably distinguish different mAbs. Moreover, it effortlessly integrates within the workflow involved in the preparation of mAbs drugs within a cancer hospital setting. This advancement holds promise for streamlining processes and enhancing efficiency in the production and use of mAbs, thereby potentially benefiting patient care and treatment outcomes.