Developing trustworthy AI systems in digital health remains a challenge, particularly in terms of explainability and reliability in clinical use. We present ParkiDxAI, a web-based clinical decision support system (CDSS) for Parkinson’s disease that integrates prediction, explanation, data storage, and communication of results. Using a real-world tabular dataset of 2,105 individuals with 32 variables, we benchmarked 10 machine-learning models on an independent test set. Within this intra-study comparison, CatBoost achieved the highest accuracy at 93.59%. ParkiDxAI provides dual-level explanations—global (SHAP) and local (LIME)—and quantifies explanation quality using faithfulness, fidelity, and sparsity. The system has been developed with a FastAPI backend, a MySQL database, and a React interface for scalable deployment. A small usability assessment suggested that the explanations and UI were clear and usable. Overall, ParkiDxAI aims to bridge the gap between model performance and clinical usability and can be adapted to other tabular clinical tasks.