Accurate, high-throughput, and unbiased analysis of plutonium oxide particles is necessary for analysis of the underlying phenomena associated with the process parameters involved in its synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology using scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. Among other candidates, a neural network called Vector Quantized Variational Autoencoder (VQ-VAE) has shown the ability to quantitatively describe particle morphology with >85% accuracy from uranium oxide processing routes. In this study, we utilized VQ-VAE to analyze synthesized plutonium dioxide (PuO2) particles to investigate the underlying phenomena and predict its process parameters. The surface morphology of PuO2 powders calcined from plutonium oxalates precipitated under varying synthesis conditions related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order was performed using SEM. A pipeline was developed to extract and quantify useful images of individual particles with VQ-VAE which was followed by further reduction of the dimensionality of the feature space using a bottlenecking neural network fit to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters for a single particle with >80% accuracy for some parameters. It also demonstrated potential in successfully grouping particles with similar surface morphology. The clustering and classification results revealed valuable information regarding the chemical process parameters that predominantly influence the PuO2 particle morphology, which includes strike order and oxalic acid feed stock, respectively. Repeating the analysis with multiple particles improved the classification accuracy of each process parameter compared to results yielded using single particle, with statistically significant results being yielded with as few as four particles.