Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/3370 2024 2024 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, Isa Town, Bahrain El El-Sayed This review aims to discuss the use of AI and ML in diagnosing and managing neurodegenerative diseases, with particular emphasis on AD and MCI. Emerging innovations present in depth the effectiveness of using ML models such as SVM, random forests, CNNs, and new frameworks such as quantum-classical neural networks on data obtained from MRI imaging, EEG signals, genetic makers and sociodemographic data. Widely used research findings demonstrate that these tools offer seemingly higher detection rates, sensitivity, and specificity than traditional diagnostic techniques for identifying and diagnosing early-stage illnesses. Some of them are techniques based on analyzing EEG time-frequency bands, combining MRI and PET data integration approaches, and creating telemedicine services to overcome geographical barriers. Furthermore, interpretable AI models improve clinical relevance by providing decision and trust support among practitioners. While these achievements are notable, the following limitations need to be noted, thus making it easier to establish the generalizability of the results and ways of using datasets that are free from bias and difficulties associated with applying AI in clinical settings. There are pressing questions regarding patients' rights and privacy, the issue of homogenization and standardization of data, and the distribution and accessibility of AI tools across industries as well as within the same region. More studies should be conducted to expand AI applications, use a more diverse dataset, and promote cooperation between representatives of various fields of science to ensure that technological advancement meets clinical demands. It also includes new methods like Vision Transformers and Quantum Computing Enhanced Deep Learning to overcome diagnostic issues in time-consuming and multi-parametric data analysis. These gaps can be closed with the help of AI and ML to enhance diagnostic accuracy, select the right treatment strategy, and risk assessment for the long-term management of NDs. In conclusion, this review similarly reaffirms how stunning AI's role is in improving future neurodegenerative disease care. For this reason, the deployment process must be done sensibly to enhance the patient's value most appropriately. 2024 2024 22 36 10.54216/MOR.010203 https://www.americaspg.com/articleinfo/41/show/3370