Parkinson’s disease (PD) is a neurodegenerative ailment that is common and develops slowly. It is also crucial to have a diagnosis before treating and managing this disease. This paper describes a new application of cutting-edge deep learning methods to detect and classify PD. In particular, it explains the combination of convolutional neural networks with convolutional long-short memory networks and support vector machines. This leads to more accurate and efficient diagnostics for PD. Here, Convolutional Neural Networks (CNNs) are used to extract the spatial features and Long Short-Term Memory networks (LSTMs) are responsible for capturing the temporal dependencies in a sequence of data. The proposed method resulted in an accuracy of 95.58%, which contributed to identifying Parkinson’s in an early stage.