Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. One of the main critical problems regarding this disease is linked to the difficulty of differentiating that from other similar diseases having common symptoms. Recent advances in wearable devices technology and in machine learning, as well as in cognitive and neuropsycological studies, have provided new methods of investigating these crucial differences. The aim of this thesis is to provide a comprehensive review of current applications where a multimodal differential diagnosis have been tested, through video, movement, voice, cognitive and electrophysiological exams, in order to properly recognize and differentiate PD from atypical parkinsonism (AP) and other critical neurodegenerative syndromes, like Alzheimer's Disease (AD).