Chordoma, a rare malignant tumor, is notably resistant to conventional treatments including chemotherapy, radiotherapy, and targeted approaches. Immunotherapy, successful in treating other cancer types, presents a promising avenue. However, the immune microenvironment of chordoma is poorly understood, highlighting the need to investigate immune checkpoints and their potential as therapeutic targets in this context. We performed an integrated analysis of chordoma using public datasets (GSE224776, GSE56183, GSE239531) and our RNA-seq data (11 samples). Differential expression analysis (limma), gene set enrichment analysis (GSEA, clusterProfiler), immune cell infiltration assessment (ESTIMATE, immunedeconv), weighted gene co-expression network analysis (WGCNA), consensus clustering, and machine learning were employed to identify key immune-related gene modules, immunogenic subtypes, and central immune regulators. Hierarchical clustering and principal component analysis segregated chordoma from control samples post quality control. Differential expression analysis identified 2825 upregulated and 1693 downregulated genes, with significant upregulation of immune checkpoints, including PD-1 and CTLA-4. GSEA highlighted enhanced immune-related processes, particularly inflammatory responses, antigen presentation, and immune cell activation. Immune cell deconvolution demonstrated selective enrichment of memory T cells and macrophages, alongside downregulation of neutrophils and decreased effector cell scores. Consensus clustering identified a highly immunogenic chordoma subtype (Cluster 1), and WGCNA and machine learning converged on CCR7 as a central immune regulator, with core T cell-associated genes correlating with immune cell distribution patterns. This study characterizes the chordoma immune landscape, highlighting elevated immune checkpoints, distinct immunogenic subtypes, and a T cell-centered regulatory network. These findings support immune checkpoint inhibitors and other immunotherapies as promising treatments.