In the medical healthcare system, a significant amount of medical and research studies, different types of tests, imaging, prescription, and medicated data on vocal and hand tremors are being accumulated and stored tremendously. Most Parkinson’s disease (PD) sufferers are afflicted by vocal cord issues and hand tremors. Speech impairment and hand tremors are early signs of Parkinson’s disease detection. The purpose of this work is to discuss the application of deep learning algorithms to improve the detection of Parkinson’s disease (PD) phases utilizing acoustic data from vocal samples and handwriting samples. PD sufferers often experience vocal cord issues and hand tremors, making these symptoms crucial for early detection. The study uses various deep learning models, including RNN, CNN, and Vision Transformer, to cite the features from the collected data. The study makes use of an extensive collection of data from the UCI machine learning repository, which includes voice and hand tremor samples from 62 Parkinson’s disease patients and 15 healthy persons. The study used three sorts of recordings: static spiral tests, dynamic spiral tests, and stability tests. The goal is to identify the best algorithm using deep learning for early Parkinson’s syndrome detection by analyzing many modalities, including motor cardinal traits (bradykinesia, stiffness, tremor, axial indications) and mobility symptoms (gait, handwriting, speech, and EMG). The EfficientNetB5 model outperforms other models with an accuracy of 97.65%, while the Vision Transformer model shows promise as an alternative to CNN models but achieves an accuracy of 94.80%, lower than EfficientNetB5. The paper concludes by proposing new guidelines for further research on deep machine learning models for the automatic detection of PD, aiming to enhance early identification of PD globally and also address the challenges in adopting contemporary computer-aided diagnosis systems in health care.