Pancreatic cancer is among the most lethal malignancies, largely due to its asymptomatic progression and the absence of effective early detection methods. Urinary extracellular vesicles (EVs) represent a promising, non-invasive source of disease-specific biomarkers. Data-Independent Acquisition (DIA) mass spectrometry enables comprehensive proteomic profiling; however, independent analyses of precursor (MS1) and fragment (MS2) ion spectra often yield inconsistent results due to differing sources of signal interference and variability. Urine samples from five pancreatic cancer patients and five healthy controls were processed to enrich EVs via ultracentrifugation. DIA-MS was performed using an Orbitrap Eclipse MS with raw data analyzed using Spectronaut 19.1. MS1 and MS2 intensities were normalized to exosome protein markers. We developed a unified linear mixed-effects model (LMM) to concurrently analyze normalized MS1 and MS2 intensities, treating the MS1 and MS2 signals as technical replicates from the same biological sample. This model estimates protein abundance differences between groups while accounting for intra-group variability. Its performance was compared with Spectronaut quantitation by benchmarking against standard MS1 or MS2 methods using both simulated data and pancreatic cancer clinical data. The unified LMM consistently outperformed single-stream analyses, identifying a balanced and accurate set of differentially abundant proteins while avoiding overestimation. Simulations confirmed the model’s robustness, achieving higher true positive rates and lower false positive rates across varying sample sizes. The model also provided conservative estimates when MS1 and MS2 data diverged. While the LMM tends to be more powerful, Spectronaut’s methods are comparatively more conservative. In clinical data, the LMM further improved pathway enrichment outcomes, offering deeper biological insights. This LMM approach enhances the reliability of differential protein analysis in DIA-based proteomics by integrating MS1 and MS2 data. Applied to urinary EVs, it enables robust biomarker discovery for pancreatic cancer detection. This method holds significant promise for advancing personalized medicine through precise, non-invasive diagnostics.