<p>Proteomics has emerged as a transformative discipline for elucidating the dynamic behavior of proteins, offering insights beyond the static frameworks of genomics and transcriptomics. In particular, protein–protein interactions (PPIs) and post-translational modifications (PTMs) represent critical layers of biological regulation that are often dysregulated in complex diseases such as cancer, neurodegeneration, and autoimmunity. This review provides an integrative analysis of how proteomics enables the characterization of disease-specific interactomes and PTM landscapes, thereby supporting the discovery of functional and clinically relevant biomarkers. A structured literature search covering studies from 2010 to 2024 was conducted across major databases to identify experimental and computational strategies for mapping PPIs and PTMs using high-resolution techniques such as mass spectrometry (MS), tandem affinity purification (TAP), and surface plasmon resonance (SPR). Emphasis is placed on the synergistic crosstalk between PTMs and PPIs, which modulates signaling networks and shapes pathological phenotypes. We further highlight how artificial intelligence (AI) and machine learning (ML) algorithms are increasingly integrated into proteomic pipelines to enhance data interpretation, biomarker prioritization, and disease stratification. Through the integration of recent advances and representative disease models, we demonstrate that proteomics not only enables biomarker identification but also provides mechanistic insight into disease pathogenesis. The challenges of clinical translation, including reproducibility, standardization, and multi-omics integration, are critically examined. Ultimately, this review advocates for a systems-level approach that leverages proteomic tools to decode disease biology and accelerate the development of precision diagnostics. </p>