The integration of multi-omics data using Artificial Intelligence (AI) is rapidly transforming the landscape of neurological disease research. As our understanding of complex brain disorders such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and schizophrenia deepens, there is an ever-increasing demand for sophisticated tools for handling and interpreting high-dimensional biological data. In this chapter, we explore the integration of AI and multi-omics in neurobiology, describing the evolution of AI techniques from classical rule-based systems to cutting-edge deep learning frameworks and foundation models. We provide a comprehensive guide to matching specific omics combinations with appropriate AI methods to answer targeted neuroscientific questions, discuss major neurological disease-specific databases and resources, and present detailed case studies that highlight the real-world potential and clinical translation of these approaches. Additionally, we address the existing technical, ethical, and interpretability challenges that must be overcome for successful clinical implementation. By the end of this chapter, readers will be equipped with both theoretical knowledge and practical guidance to choose and apply appropriate AI tools in multi-omics neurological disease research.