Biocatalysis has rapidly become an essential tool in the scientific and industrial communities for the development of efficient, safe, and sustainable chemical syntheses. Immobilization of the biocatalyst, typically an engineered enzyme, offers significant advantages, including increased enzyme stability and control, resistance to environmental change, and enhanced reusability. Determination and optimization of the spatial and chemical distribution of immobilized enzymes are critical for proper functionality; however, analytical methods currently employed for doing so are frequently inadequate. Machine learning, in the form of multivariate curve resolution, with Raman hyperspectral imaging is presented herein as a potential method for investigating the spatial and chemical distribution of evolved pantothenate kinase immobilized onto two diverse, microporous resins. An exhaustive analysis indicates that this method can successfully resolve, both spatially and spectrally, all chemical species involved in enzyme immobilization, including the enzyme, both resins, and other key components. Quantitation of the spatial coverage of immobilized enzymes, a key parameter used for process development, was accomplished. Optimal analytical parameters were determined by the evaluation of different excitation wavelengths. Exploratory chemometric approaches, including principal component analysis, were utilized to investigate the chemical species embedded within the data sets and their relationships. The totality of this information can be utilized for an enhanced understanding of enzyme immobilization processes and can allow for the further implementation of biocatalysis within the scientific and pharmaceutical communities.