This study explores the use of machine learning models to assess the severity of Parkinson's disease symptoms based on data from wearable and smartphone sensors. It presents models to predict the severities of individual symptoms-tremor, bradykinesia, stiffness, and dyskinesia-as well as the overall state of patients, using both clinician and patient self-assessments as labels. The dataset, although limited and imbalanced, enabled the identification of key trends. The best performance was achieved when combining data from both the MYO armband and smartphone, and when using patient self-assessments as targets. Tremor was the most predictable symptom, while others proved more challenging-especially at higher severity levels, which were poorly represented in the dataset. These results highlight the value of multimodal data and the importance of patient input in symptom monitoring. However, they also point to the need for more balanced and extensive datasets to improve prediction accuracy across all severity levels and symptoms.