Early diagnosis of voice pathology in neurodegenerative disorders is essential for proper management, reducing complications, and limiting the progression of these disorders. Artificial intelligence (AI) for voice analysis has been utilized for identifying and categorizing patients with disorders such as Alzheimer’s disease and multiple sclerosis. AI is imposing itself as a promising tool for early detection, staging, and progression monitoring of these disorders. The fidelity of the voice samples, the computerized programs utilized for analysis, and the tools of AI chosen for learning contribute to the success of AI-enhanced voice analysis to help clinicians optimize the management options for patients. Deep learning (DL) approaches have been found to overcome the limitations of the usual machine learning algorithms in health care. The AI-enhanced voice analysis using the DL models has the advantage of being a cost-effective and non-invasive technology, which could cope with cross-language differences.