• Manisha M Chaudhari
• Mukta Agrawal

Research on employing speech and voice analysis to identify neurodegenerative illnesses like Alzheimer's disease has increase suddenly in the last ten years. Many studies indicate the importance of specific auditory characteristics in recognizing Alzheimer's disease from normal aging-related memory problems and speech analysis is believed to be an inexpensive method of diagnosing dementia. Furthermore, without proper care, Alzheimer's disease (AD), a brain condition, is incurable. Consequently, recognizing AD is essential in halting and controlling its progression. The traditional approach diagnoses AD based on tests that are empirical like MRI results, and the patient's medical history. Although this method works well, it is imprecise. The goal of this analysis is to look at the numerous approaches to diagnosing and categorizing Alzheimer's disease that have been applied to different datasets. The main contribution of this work is a summary of previous research, understanding gaps, relevant obstacles, and suggestions for additional study.

Alzheimer's disease, AI-ML Algorithms, Image, Text, Audio and Transcript dataset

"Exploration Alzheimer’s disease prediction using image , text ,voice and transcripts dataset - Revised", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.110-117, December-2025, Available :http://www.jetir.org/papers/JETIRHE06014.pdf

"Exploration Alzheimer’s disease prediction using image , text ,voice and transcripts dataset - Revised", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. pp110-117, December-2025, Available at : http://www.jetir.org/papers/JETIRHE06014.pdf