Parkinson’s disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this disease early is challenging because it depends on traditional diagnostic methods that face issues of subjectivity, which commonly delay diagnosis. Several objective analyses are currently in practice to help overcome the challenges of subjectivity; however, a proper explanation of these analyses is still lacking. While machine learning (ML) has demonstrated potential in supporting PD diagnosis, existing approaches often rely on subjective reports only and lack interpretability for individualized risk estimation. This study proposed SCOPE-PD, an explainable AI-based prediction framework by integrating subjective and objective assessments to provide personalized health decisions. Subjective and objective clinical assessment data are collected from the Parkinson’s Progression Markers Initiative (PPMI) study to construct a multimodal prediction framework. Several ML techniques were applied to these data, and the best ML model was selected to interpret the results. Model interpretability was examined using SHAP-based analysis. The Random Forest algorithm achieved the highest accuracy of 98.66% while working with the combined features from both subjective and objective test data. Tremor, bradykinesia, and facial expression are identified as the top three contributing features from the MDS-UPDRS test in the prediction of PD.