Parkinson’s disease (PD) is a progressive neurological illness that needs to be diagnosed early and accurately in order to be effectively managed and improve patient outcomes. This study focuses on the developments in Explainable Artificial Intelligence (XAI) and machine learning (ML) methods used for PD identification with multimodal, handwriting, and voice datasets. Incremental learning techniques, including Incremental Support Vector Machines, have demonstrated efficacy in predicting UPDRS scores, overcoming the difficulty of updating predictive models without starting over from scratch. Using advanced feature selection approaches and strategies like SMOTE to handle imbalanced datasets, ensemble techniques like XGBoost-Random Forest and Nearest Neighbor Boosting (NNB) have shown great diagnostic accuracy. In order to improve accuracy and lower computational costs, nature-inspired optimization techniques, such eagle-inspired algorithms, have been developed to optimize feature selection in speech signal analysis. By lowering the dimensionality of the data while preserving feature integrity, these models have shown promise in improving diagnostic performance. Furthermore, the effectiveness of these techniques is demonstrated by performance indicators such as sensitivity, accuracy, and area under the curve (AUC). The revolutionary impact of ML and XAI in PD diagnosis is highlighted in this study, with a focus on interpretability, computational efficiency, and multimodal integration as crucial elements for clinical adoption. These developments open the door to more reliable and expandable AI-powered medical treatments for neurodegenerative diseases.