Spatial transcriptomics (ST) is emerging as a powerful technology that transforms our understanding of thyroid cancer by offering a spatial context of gene expression within the tumor tissue. In this review, we synthesize the recent applications of ST in thyroid cancer research, with a particular focus on the heterogeneity of the tumor microenvironment, tumor evolution, and cellular interactions. Studies have leveraged the spatial information provided by ST to map distinct cell types and expression patterns of genes and pathways across the different regions of thyroid cancer samples. The spatial context also allows a closer examination of invasion and metastasis, especially through the dysregulation at the tumor leading edge. Additionally, signaling pathways are inferred at a more accurate level through the spatial proximity of ligands and receptors. We also discuss the limitations that need to be overcome, including technical limitations like low resolution and sequencing depth, the need for high-quality samples, and complex data handling processes, and suggest future directions for a wider and more efficient application of ST in advancing personalized treatment of thyroid cancer.