Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the three chronic subtypes of myeloproliferative neoplasms (MPNs), we identify the blood platelet transcriptome as a proxy strategy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine-learning algorithms. The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy. Subtype-specific markers offer mechanistic and therapeutic insights, and highlight impaired proteostasis and a persistent integrated stress response. Using a LASSO model with validation in two independent cohorts, we identify the advanced subtype MF at high accuracy and offer a robust progression signature toward clinical translation. Our platelet transcriptome snapshot of chronic MPNs demonstrates a proof-of-principle for disease risk stratification and progression beyond genetic data alone, with potential utility in other progressive disorders.