Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline. First, longitudinal follow-up data of 210 dementia patients were used to create fast and slow progression groups. Secondly, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates. Lastly, we explored the progression biomarker potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF β-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways, which could indicate a lack of a protective response in these individuals. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.