This study explores the application of Artificial Neural Networks for the detection and diagnosis of Parkinson's Disease. Parkinson's Disease is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms that severely affect patients' quality of life. Traditional diagnostic methods rely heavily on clinical evaluations, which can be subjective and may delay diagnosis. Our research employs ANNs to improve early detection accuracy by analyzing various datasets, including voice and gait parameters. The ANN models used in this study demonstrated high accuracy rates in distinguishing PD patients from healthy controls. The results indicate that ANNs can effectively identify subtle patterns and features associated with PD, providing a more objective and timely diagnostic tool. This has significant implications for improving patient outcomes through earlier and more accurate detection. Future research should focus on expanding the dataset and integrating multimodal data to enhance the robustness and applicability of ANN-based diagnostics in clinical settings.