Effective project management approaches were conceived in as early as 1950s through the development of various methods such as PERT and CPM. However, the success of a project does not solely rely on project management methods alone. And the rightful connotation for a successful project isn’t one that did not experience any delays but rather one that is successful in mitigating the risks that arose during its execution. This study aims to solidify the risk management methods involved within a project management process and facilitate the shift towards a proactive risk management by following the Risk Management Framework namely: The study concludes that throughout the features involved within the dataset, the relevant features for the risk severity prediction include Days away from both forecast and baseline, criticality of submission, whether a submission is overdue, previous risk of its predecessor, phase, milestone, and discipline. The Best Random Forest algorithm returned a 99.41% accuracy through the fivefold validation process, indicating strong model performance and its effectiveness in predicting risk severity. Furthermore, the model was used in 3 different post-prediction iterations with feature tuning to identify and suggest possible risk mitigating strategies for high severity risks. words.