Early and accurate diagnosis is critical for managing Parkinson’s Disease (PD), is a condition that affects the motor and non-motor functions. Conventional diagnostic approaches are subjective and lengthy. Machine learning (ML) offers a potential avenue for enhancing diagnostic precision by utilizing diverse data inputs. This research explores the efficacy of ML techniques—KNN, Random Forest, and XGBoost—in identifying PD through motor and vocal characteristics. Speech impairments and handwriting evaluations were integrated to facilitate earlier detection and improve prediction. Results indicate that XGBoost achieved superior performance, demonstrating an accuracy of 88% for vocal features and 80% for wave pattern data, with F1 scores of 0.92 and 0.90, respectively. The findings show that voice pattern analysis is a more objective evaluation method compared with voice data alone. K-fold cross-validation enhances model reliability; the XGBoost model accuracy was 87.5% for voice and 86.8% for wave data. These outcomes suggest improved patient treatment and reinforce ML’s capabilities in refining PD diagnosis. Integrating vocal and motor data offers a more holistic approach than single-modality methods, leading to better diagnostic outcomes and optimized care strategies for those affected by PD.