Abstract: Post-translational modifications (PTMs) function as fundamental molecular switches that govern protein activity and exponentially expand the functional complexity of the proteome far beyond the genomic blueprint. These modifications play pivotal and multifaceted roles in regulating essential biological processes, including cellular signal transduction, metabolic homeostasis, and the initiation and progression of various pathogenic conditions. However, the comprehensive and in-depth characterization of the PTM landscape remains a formidable analytical challenge. This difficulty stems primarily from the inherent physicochemical characteristics of PTMs, such as their substoichiometric abundance, the wide dynamic range in biological samples, and significant structural heterogeneity—a complexity that is particularly pronounced in the analysis of intricate modifications like glycosylation. While mass spectrometry (MS)-based proteomics has emerged as the dominant technology in this field, traditional analytical workflows are frequently hampered by significant technical bottlenecks. These limitations are most evident in the lack of enrichment specificity, ambiguous site localization, and a heavy dependency on incomplete reference databases. This review systematically summarized the critical biological functions of key PTMs, including phosphorylation, glycosylation, and lysine acylation, while also providing a critical evaluation of classical analytical strategies and their inherent limitations. Building on this foundation, the transformative potential of artificial intelligence (AI)—specifically advanced deep learning algorithms—as a revolutionary force capable of breaking through these longstanding technical barriers was focused on in our discussion. The ways in which AI-driven computational paradigms are reshaping the field were elaborated in detail by enabling the construction of high-precision virtual spectral libraries, optimizing site localization algorithms to rigorously control false localization rates, and facilitating the database-independent identification of complex glycopeptides. Collectively, these advancements result in a significant enhancement in the depth, coverage, and accuracy of PTM analysis. Furthermore, this article elucidated the practical utility of these emerging technologies in clinical translation. Breakthrough progress in specific applications was highlighted, such as the screening of diagnostic glycosignatures in urinary extracellular vesicles for bladder cancer, the decoding of dysregulated phosphorylation signaling networks in KRAS-mutant lung cancer, and the elucidation of molecular mechanisms underlying drug resistance in pancreatic cancer. Looking forward, the deep integration of AI algorithms with single-cell proteomics and multi-omics data, coupled with the continuous development of novel enrichment materials, will be instrumental in decoding PTM regulatory networks. This convergence promises to systematically reveal the spatiotemporal dynamics of PTMs, thereby providing robust technical support for early diagnosis and the development of targeted therapies in the era of precision medicine.