Abstract: Tumor neoantigens, arising from tumor-specific genetic and post-transcriptional alterations, represent highly attractive targets for precision cancer immunotherapy due to their strict tumor specificity and low risk of off-target toxicity. Accurate identification of neoantigens presented on major histocompatibility complex (MHC) molecules remains a central challenge, as most candidate neoantigens predicted from genomic or transcriptomic data are not ultimately processed and presented on the cell surface. Mass spectrometry (MS)-based immunopeptidomics is currently the only experimental approach capable of directly and unbiasedly identifying naturally presented MHC-bound peptides, thereby providing critical evidence for validating tumor neoantigens at the protein functional level. Over the past decade, substantial advances in MS instrumentation, ion mobility separation, and data acquisition strategies have markedly improved the depth, sensitivity, and quantitative robustness of immunopeptidomic analysis. These developments have enabled systematic characterization of tumor antigen landscapes from increasingly limited biological material and have accelerated the transition of neoantigen discovery from computational prediction to experimental verification. In this review, the current methodological framework of MS-based immunopeptidomics for tumor neoantigen identification was comprehensively summarized, with an emphasis on key technical steps, recent innovations, and remaining challenges. The immunopeptidome sample preparation strategies were first discussed, focusing on mild acid elution and immunoaffinity purification. While mild acid elution offers simplicity and scalability, its limited specificity and susceptibility to contaminating peptides restrict its application, particularly for complex tissue samples. Immunoaffinity purification using anti-MHC antibodies remains the gold standard due to its high specificity and ability to capture native MHC-peptide complexes, although issues related to sample loss, throughput, and reproducibility persist. Emerging microfluidic and automated platforms have shown promise in addressing these limitations and enabling high-sensitivity analysis of scarce clinical specimens. Next, recent progress in mass spectrometric detection technologies that are tailored for immunopeptidomics was reviewed. Chemical derivatization and isobaric labeling strategies have been developed to enhance peptide ionization efficiency, chromatographic performance, and quantitative accuracy. Advances in MS design, including ion trapping, high-duty-cycle detection, and ion mobility separation (IMS), have substantially improved the detectability of low-abundance immunopeptides. Modern platforms integrating ion mobility with rapid fragmentation schemes now support high-coverage immunopeptidome profiling at low cellular inputs, thereby expanding the applicability of immunopeptidomics to clinically relevant samples. The data acquisition and analysis strategies were further examined, highlighting the complementary roles of data-dependent acquisition (DDA), data-independent acquisition (DIA), and targeted mass spectrometry. DDA remains indispensable for high-confidence peptide identification and spectral library construction, whereas DIA offers improved reproducibility and quantitative consistency. Hybrid workflows combining DDA-derived spectral libraries with DIA analysis currently represent the most reliable approach for large-scale immunopeptidomic studies. Targeted mass spectrometry techniques, supported by isotopically labeled standards, enable absolute quantification and validation of selected neoantigens, providing a critical bridge to translational and clinical applications. Finally, the database construction strategies for neoantigen discovery were summarized, including mutation-derived databases, non-canonical translation databases, and post-translational modification-specific databases. Integration of multi-omics data has greatly expanded the identifiable antigen repertoire beyond conventional coding mutations, revealing neoantigens originating from RNA editing, alternative translation events, and modified peptides. Despite these advances, challenges remain in database size control, false discovery rate (FDR) management, and functional immunogenicity validation. In conclusion, MS-based immunopeptidomics has evolved into a central technology for experimentally grounded tumor neoantigen discovery. Continued progress in instrumentation, computational analysis, and standardized workflows is expected to further enhance sensitivity, reproducibility, and clinical feasibility. Coupled with artificial intelligence-driven data interpretation and integrative multi-omics approaches, immunopeptidomics is poised to play an increasingly important role in precision immunotherapy and personalized cancer vaccine development.