The human gut microbiota plays a pivotal role in maintaining host immunity, regulating metabolism, and sustaining neurophysiological homeostasis. Increasing evidence implicates gut dysbiosis in the onset and progression of neurodegenerative disorders (NDDs), including Alzheimer's and Parkinson's disease, primarily through the gut-brain axis. Recent advances in high-throughput sequencing and multi-omics technologies, such as metagenomics, metabolomics, and metaproteomics have generated vast datasets, yet their clinical translation remains hindered by data heterogeneity, analytical complexity, and the absence of standardized workflows. Disjointed findings across studies underscore the urgent need for reproducible pipelines and integrative computational strategies. This review presents a comprehensive framework that leverages artificial intelligence (AI) and machine learning (ML) for systematic microbiome investigation in NDDs. We highlight how multi-omics integration with AI improves the resolution of host-microbiome interactions, while standardized preprocessing workflows ensure reproducibility and comparability across datasets. The role of explainable AI is emphasized in enhancing interpretability, improving biomarker discovery, and fostering trust in predictive models. We further examine the emerging field of pharmacomicrobiomics, where ML-driven approaches support the development of precision therapies tailored to microbiome-drug interactions in neurodegeneration. Sophisticated models, including random forests (RF), neural networks, and transfer learning, are critically assessed for predictive diagnostics, therapeutic target identification, and cross-cohort generalizability. Finally, the review proposes a roadmap to address current barriers, particularly challenges of heterogeneity and reproducibility, and advocates for validated pipelines and interdisciplinary collaboration. Collectively, AI-driven multi-omics strategies hold transformative potential for advancing microbiome-based precision medicine in NDDs.
