Intrinsically disordered proteins (IDPs), defined by their lack of stable tertiary structures, play pivotal roles in cancer development by modulating signaling cascades, transcriptional activity, and essential cellular processes. In the present analysis, experimentally validated cancer-associated IDPs from the DisProt database were systematically categorized into Fully Intrinsically Disordered Proteins (FIDPs, ≥90% disorder), Moderately Intrinsically Disordered Proteins (MIDPs, 30–90% disorder), and Ordered Proteins (ODPs, <30% disorder). A multi-layered computational framework, incorporating machine learning-based tools including PONDR, PONDR-DEPP, Depictor2, FuzDrop, and STRING, enabled functional profiling of these classes. FIDPs demonstrated a higher density of molecular recognition features (MoRFs), increased phosphorylation site prediction, and elevated liquid–liquid phase separation (LLPS) propensity compared to MIDPs and ODPs. Protein–protein interaction network analysis via STRING revealed that FIDPs are frequently positioned as central hubs in networks governing apoptosis, DNA repair, and cell cycle regulation. Furthermore, detailed mapping identified functionally relevant disordered regions enriched with predicted phosphorylation hotspots, MoRF segments, and LLPS-favorable domains. These intrinsically disordered segments reflect a high potential for dynamic regulation and molecular adaptability. The annotated IDR sites possess notable translational relevance, offering promising avenues for the design of natural disordered-based biosensors, the development of disorder-targeted therapeutics, and the exploitation of post-translational modification (PTM) patterns in cancer diagnostics and treatment strategies.