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Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised histologically by the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of Lewy bodies in various parts of the brain. The SNpc is a compact structure in the midbrain that plays a vital role in motor coordination and movement control by producing a chemical substance called dopamine, which is integral for controlling the initiation, velocity, and fluidity of voluntary movement sequences. The causes of most cases of PD (known as ‘sporadic’ or ‘idiopathic’ PD) are still unknown but involve complex interactions between genetic and environmental factors. PD is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting 1% of the population over the age of 60 and reaching approximately 5% at 85. The prevalence is rising due to ageing populations. According to the Parkinson Disease Foundation, about 10 million people worldwide have PD, one million of them in the USA, 1.2 million in Europe, and two million projected in China by 2030. One out of 500 individuals in the UK are affected, and it is expected that this number will rise threefold in the next 50 years. There is currently no proven disease-modifying therapy. The diagnosis of PD requires the presence of bradykinesia (slowness of movements) in addition to muscle rigidity or tremor or postural instability. Approximately 20% of patients do not develop a tremor. The manifestations of PD are not limited to motor impairments. Prompt diagnosis of PD is important in order to provide patients with appropriate treatment and information on prognosis. However, an accurate early diagnosis can be challenging because the movement symptoms can overlap with other conditions. Doctors make the diagnosis of PD based on clinical evaluation, interpreting information gained predominantly through history-taking and examination of the patient. Sometimes brain imaging may be requested to help support the clinical diagnosis, but there are currently no tests that are wholly sensitive or specific for Parkinson’s. The rate of misdiagnosis of PD is approximately 10–25%, and the average time required to achieve 90% accuracy is 2.9 years. Autopsy is still the gold standard for the confirmation of the disease. Therefore, this project designed an advanced convolution neural network model to predict Parkinson disease from both image and voice data. In general, existing ML algorithms such as SVM, and Random Forest will not filter data multiple times so its prediction accuracy is less hence CNN is used in this project, which filter data multiple times using neuron values so its prediction accuracy can be better. This project uses WAVE and SINE images of normal and Parkinson disease patients for imaging data and UCI Parkinson recorded voice is used for voice samples. [ABSTRACT FROM AUTHOR]