Bioplastics derived from renewable biomass are emerging alternatives to conventional petroleum-based plastics, but their viability as replacements requires thorough molecular analysis. Raman spectroscopy (RS) has emerged as a technique that is optimal for this purpose because of its advantageous properties, such as being rapid, nondestructive, and label-free. It facilitates basic material identification for advanced analyses of the structure, namely, the crystallinity, polymorphism, molecular orientation, and influence of fillers, as well as processing and degradation from the spectral shifts and peak intensity changes that occur. During the synthesis and processing of bioplastics, RS tracks monomer-to-polymer conversion and additive dispersion with quantitative, real-time feedback and a minimal lag based on the ester and carboxyl bands. The application of the technique extends to guiding formulation strategies by quantifying plasticizers and fillers. Utilization of extensive spectral libraries, routine calibration, and integration of machine learning (ML) or deep learning (DL) algorithms to automate feature extraction as well as denoising enables univariate calibrations and multivariate chemometrics such as principal component analysis, partial least squares, linear discriminant analysis, and multivariate curve resolution. Ongoing advances, such as surface-enhanced Raman scattering, Fourier transform-Raman, and Raman tomography, pave the way for overcoming several challenges, such as low-scattering cross-sections, fluorescence interference, and overlapping bands, and, therefore, more promising, quantitative bioplastic research.