Parkinson's disease (PD) presents significant challenges in early detection, often relying on subjective clinical assessments and costly imaging procedures, leading to low accuracy rates, especially in detecting PD at its early stages. This research investigates the transformative potential of machine learning and deep learning algorithms in PD detection and diagnosis. Leveraging a diverse dataset comprising vocal fundamental frequency measurements, nonlinear dynamical complexity measures, and image data, we explore various ML/DL models. Our findings reveal promising accuracies across different models, with DenseNet121 achieving a maximum accuracy of 96.67% for image data and hybrid architectures like 1D CNNGRU achieving 94.87% accuracy for vocal data. Furthermore, the study identifies key limitations and challenges associated with each model, emphasizing the need for continued refinement and innovation. This research contributes to advancing PD diagnosis, offering potential improvements in patient outcomes and paving the way for more accessible and effective diagnostic solutions.