The paper presents an idea that combines machine learning and signal processing approaches to tackle the difficulties associated with detecting Parkinson illness. Early identification is crucial for optimal therapy of Parkinson, a degenerative neurological disorder characterized by tremors, stiffness, and balance problems. Our technique attempts to improve diagnosis accuracy by preprocessing speech recordings and using spectrogram graphics to detect vocal deficiencies associated with Parkinson illness. Positive outcomes are obtained via training and evaluating four distinct algorithms: the transformer based BEiT model, XGBoost, Multilayer Perceptron (MLP), and Support Vector Machine (SVM). It is noteworthy that the BEiT model has the highest accuracy of 90%, outperforming SVM (S6%), MLP (66%), and XGBoost (53%). The results emphasize the capability of deep learning techniques, namely BEiT, to expedite the diagnosis of Parkinson illness, enable customized treatment, and prompt timely action, possibly enhancing patient outcomes and quality of life. Additional study is necessary to confirm the effectiveness of the approach on larger and more varied datasets and evaluate its feasibility in clinical environments.