The non-random usage of synonymous codons encoding the same amino acid—referred to as codon usage bias (CUB)—varies substantially across genomes and significantly affects translational efficiency by modulating transcriptional and post-transcriptional processes. In chloroplast genomes, the optimization of CUB is critical for improving the efficacy of genetic engineering approaches. However, comprehensive analyses of CUB in Fagopyrum chloroplast genomes remain scarce. In this study, we performed an in-depth comparative analysis of codon usage patterns in the chloroplast genomes of nine Fagopyrum species. Our results revealed a marked AT-rich nucleotide composition, with base content in the order T > A > C > G. We identified 23 optimal codons and 29 high-frequency codons, most of which ended with A or U. Correlation analyses demonstrated that codon usage is strongly influenced by nucleotide skewness (GC and AT skews), protein properties (such as amino acid composition and the number of synonymous codons), and gene expression levels. Neutrality plot analysis (PR2 bias) and evaluations based on the effective number of codons (ENc) indicated that both mutational pressure and natural selection contribute to shaping CUB, with natural selection identified as the predominant evolutionary force. Comparative analyses with four model organisms indicated that Arabidopsis thaliana shares the highest codon usage compatibility with Fagopyrum chloroplast genomes, highlighting its suitability as a potential heterologous expression system. Phylogenetic reconstruction based on codon usage profiles yielded a fully resolved topology with 100% bootstrap support at all nodes, reinforcing the utility of codon usage data in evolutionary inference. This study elucidates the evolutionary determinants of codon usage variation in Fagopyrum plastomes and provides a robust methodological foundation for codon optimization in chloroplast-based synthetic biology. The validated codon adaptation metrics offer promising tools for improving heterologous protein expression and guiding transgene design in advanced breeding strategies.