Parkinson’s disease (PD) and other neurodegenerative disorders are on the rise, and this implies that there must be suitable diagnostic methods that can assess and or monitor the progress of the disease. Many patients suffering from PD show signs of voice impairment; hence, voice analysis has been found to have a purpose in clinical diagnosis and assessment. This review paper looks at voice impairment and technologies that have improved it, including machine learning and deep learning to assess the voice features of the disease PD. While systematically assessing the various studies, we focus on the issues and challenges in the scope of the current methodologies. Our focus is on using voice analysis more in clinical assessments. In addition, we also outline research prospects, such as the development of microwave technologies, which can be used along with vocalization for better diagnosis and treatment of patients. This work aims to review the geometrical strategies and assessment techniques of voice analysis, which have been bent in the previous studies on PD and look at their contrast with voice analysis methods with presumptions of the effectiveness of the clinical application.