Falls in people with Multiple Sclerosis (PwMS) is a serious issue. It can lead to a lot of problems including sustaining injuries, losing consciousness and hospitalization. Having a model that can predict the probability of these falls and the factors correlated with them and can help caregivers and family members to have a clearer understanding of the risks of falling and proactively minimizing them. We used historical data and machine learning algorithms to predict three outcomes: falling, sustaining injuries and injury types caused by falling in PwMS. The training dataset for this study includes 606 examples of monthly readings. The predictive attributes are the following: Expanded Disability Status Scale (EDSS), years passed since the diagnosis of MS, age of participants in the beginning of the experiment, participants' gender, type of MS and season (or month). Two types of algorithms, decision tree and gradient boosted trees (GBT) algorithm, were used to train six models to answer these three outcomes. After the models were trained their accuracy was evaluated using cross-validation. The models had a high accuracy with some exceeding 90%. We did not limit model evaluation to one-number assessments and studied the confusion matrices of the models as well. The GBT had a higher class recall and smaller number of underestimations, which make it a more reliable model. The methodology proposed in this study and its findings can help in developing better decision-support tools to assist PwMS.