Development of plant-based products faces challenges like raw material standardization and time-consuming functionality measurements. Fourier Transform Infrared (FTIR) spectroscopy provides a quick, non-destructive way to analyze protein molecular characteristics. This study explored the classification and predictive capability of FTIR on analyzing five plant protein isolates from soy, mung bean, pea, fava bean, and lentil. Principal Component Analysis (PCA) revealed distinct clusters for each protein source based on their FTIR spectra, indicating molecular differences. Partial Least Square (PLS) regression analysis links FTIR spectra to functional properties like water absorption capacity (WAC), oil absorption capacity (OAC), solubility, foaming, and emulsification. WAC and OAC prediction models showed strong correlations, with prediction correlation coefficients (Rp2) of more than 0.97 and cross validation correlation coefficients (Rcv2) 0.84 and 0.76, respectively. As sample size increases, solubility, emulsifying, and foaming properties display promising potential. Moreover, WAC and OAC predictions exhibited robust results with protein blends of various ratios. The expanded WAC model predicted with an Rp2 of 0.99 and an Rcv2 of 0.92, while the expanded OAC model had an Rp2 of 0.92 and an Rcv2 of 0.72. The results underscore FTIR has the potential to identify plant proteins aiding in raw material verification and quality control as well as being an alternative to analyzing functional properties of plant proteins. In addition, the analysis of real food products emphasis the influence of extraction procedures and processing methods on the structural properties of protein isolates. These findings enhance our understanding of plant-based protein analysis and have important implications for improving the quality, consistency, and functionality of plant-based products within the food industry.