Genetic frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease primarily caused by pathogenic mutuations in one of three genes: \textit{C9orf72}, \textit{MAPT}, and \textit{GRN}. Accurately predicting time-to-symptom-onset could improve clinical care and patient stratification in clinical trials. This study aimed to develop a machine learning framework for individualized prediction of symptom onset in genetic FTD using multimodal MRI data under limited sample size conditions. We explored two strategies: (i) a support vector machine (SVM) classifier distinguishing symptomatic FTD from non-FTD scans, using the distance to the decision boundary (DDB) as a proxy for time-to-symptom-onset. While DDB values increased as conversion approached, they lacked precision for individual-level prediction. (ii) Five binary classifiers, each trained to predict conversion within a different time window (1–5 years) before symptom onset. Combining outputs from these classifiers yielded personalized onset predictions with a mean absolute error (MAE) of 0.71 years, outperforming a linear elastic-net regression baseline (MAE = 2.97 years). This approach successfully predicted symptoms five years prior to conversion, which represents an important step toward personalized medicine in genetic FTD.