The identification and management of Multiple Sclerosis (MS) patients who are at risk of disease progression and/or conversion to Secondary Progressive MS (SPMS) is a significant unmet clinical need. This study aimed to develop machine and deep learning (ML/DL) models to predict disability progression and/or conversion to SPMS at least 6 months before progression, determined by Expanded Disability Status Scale (EDSS) score. Three traditional ML algorithms were trained on brain parcellation volume measurements derived from T1 weighted magnetic resonance images (MRI) and convolutional neural networks (CNNs) were trained directly on MRI images. The results showed that the three ML models performed slightly better than a random classifier, with the Support Vector Classifier achieving the best results in terms of area under the receiver operating characteristic curve (AUROC) with a score of 0.62. The CNN approach yielded the most promising results, with an AUROC score of 0.75. This suggests that the ability to identify MS patients at high risk of future disability progression directly from MRI scans is a promising research direction. Further development of these algorithms could enable timely interventions, potentially preventing or delaying the onset of disability progression and/or SPMS.