Metabolomics has emerged as a powerful tool for identifying biochemical alterations in disease, revealing key pathways involved in pathophysiology. However, the role of intrinsically disordered proteins (IDPs) in these metabolomics-altered pathways remains largely unexplored, despite their established involvement in signaling, regulation, and drug resistance. This study aims to identify and characterize IDPs present within disease-associated pathways defined by metabolomics datasets, establishing a foundation for future therapeutic targeting. Public metabolomics datasets from the Human Metabolome Database (HMDB) and MetaboLights were screened to identify significantly altered metabolites for selected diseases. Pathway mapping was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) to retrieve associated proteins. These proteins were analyzed for intrinsic disorder using complementary IDP analysis protocols, IUPred3, IDPpred and flDPnn to ensure robustness. Across the analyzed disease pathways, a total of 186 proteins were retrieved, of which 57 (nearly 31%) exhibited high-disorder propensity (more than 30% residues disordered). Proteins exhibiting disorder were further annotated for biological function and disease relevance using UniProt and STRING. Among these, 18 IDPs were directly linked to metabolic regulation, 12 to stress response, and 9 to cell signaling. Several high-disorder proteins were identified in key altered pathways, including those linked to metabolic regulation, stress response, and cell signaling. Notably, many of these proteins lack structural characterization, underscoring their potential as underexplored therapeutic targets. By integrating metabolomics-driven pathway mapping with computational disorder prediction, this approach provides a novel pipeline for prioritizing IDPs in disease biology and lays the groundwork for subsequent virtual screening and drug repurposing efforts.