Parkinson’s disease (PD) is significant global health concern. As Parkinson’s disease (PWP) patients face speech and locomotion challenges, the complexities of this neurological condition frequently make customized medication and monitoring as a difficult task. However, for those who are trying to get their lives back to normal, early detection and intervention will provide some hope. As the population ages, there is a need for novel approaches that enable early, accurate, distant, and predictive Parkinson’s disease (PD) diagnosis. This work explores the use of deep learning (DL) and machine learning (ML) techniques in telemedicine to expedite the diagnosis of Parkinson’s disease. To enhance the detection process of Parkinson’s disease, deep learning (DL) techniques, like ANN, CNN, and ResNet CNN, were implemented. A new model ConvNeXt is also implemented to enhance the PD diagnosis. The results reveals ANN as the most effective method for PD identification after comparing all classification results. The result highlights how DL-based methods can completely change how telemedicine approaches disease detection. Additionally, this research highlights how critical it is for healthcare experts, data scientists, and technologists to advance telemedicine solutions for Parkinson’s assessment and treatment.