Parkinson’s disease (PD) is a complicated neurological condition that calls for a precise diagnosis as well as practical treatment plans. The present state of explainable artificial intelligence (XAI) treatments for Parkinson’s disease (PD) is examined in this literature review. In order to find the key aspects, different XAI approaches have been examined. Those approaches are generally used to improve the interpretability and transparency of AI models for Parkinson’s disease diagnosis and progression forecasting. Important research is analyzed to show the benefits, drawbacks, and clinical suitability of various XAI techniques. The analysis emphasizes how crucial explainability is to building physician confidence and making it easier to incorporate AI tools into clinical practice. Additionally, the gaps in the existing research are discussed and future directions are proposed.