Neurodegenerative diseases, under the broad-minded term of the loss of neuronal tissues from the brain, cause disorders in cognition as well as motor abnormalities significantly affecting the value of the life for patients. It was clear that there would be a need for increasingly accurate diagnostics, effective treatments, as well as new insights about disease mechanisms considering the continued rise in worldwide incidence rates. Artificial Intelligence and Machine Learning have emerged as this transformative knowledge. This chapter explores the work of AI in understanding, detecting, and managing neurodegenerative diseases, with specific focus on the capabilities of deep learning algorithms, Graph Neural Networks (GNNs), and other advanced models. These technologies allow the analysis of diverse datasets, such as neuroimaging, genomic data, and electronic health records, to identify patterns, biomarkers, and disease trajectories. AI also allows for personalized care by predicting the progression of diseases and responses to treatments, opening the way for interventions tailored to individual needs. Case studies in real-life applications show practical insights into how AI models have been successfully deployed in clinical settings. Finally, we examine emerging opportunities, such as AI-driven drug discovery and novel neuroimaging techniques, which will change the paradigm in managing neurodegenerative diseases. However, issues with AI integration include data superiority, interpretability, algorithmic biases, and ethical and regulatory considerations. Responsible leveraging of AI and ML and the overcoming of these barriers can revolutionize the diagnosis, treatment, and understanding of neurodegenerative disorders for healthcare professionals and researchers. This chapter will be a testament to the potential that AI has in improving patient outcomes in this complex new frontier of healthcare.