In the wide range of human diseases, Parkinson’s Disease has a high incidence according to the recent survey of WHO (World Health Organization). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This disorder is accompanied by a wide range of motor and non-motor impairments symptoms. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V MCU was designed for the voice-controlled human–machine Interface (HMI). With the help of signal processing and feature extraction methods, digital signal processing (DSP) algorithms can be used to extract speech signals. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals into normal or abnormal to identify PD. To analyze data, develop algorithms and create modules, we use Matrix Laboratory. We used MATLAB for algorithm development, the RISC-V processor for embedded implementation, and machine learning techniques to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs)