Neurodegenerative diseases, particularly Alzheimer's disease and Parkinson's disease, present formidable challenges in modern medicine due to their complex pathologies and the absence of curative treatments. Despite advances in symptomatic management, early diagnosis remains essential for mitigating disease progression and improving patient outcomes. Traditional diagnostic methods, such as MRI, PET, and cerebrospinal fluid biomarker analysis, are often inadequate for the early detection of these diseases. Emerging porous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), MXene, zeolites, and porous silicon, offer promising new approaches for the early diagnosis of neurodegenerative diseases. These materials, characterized by highly tunable physicochemical properties, have the potential to capture and concentrate disease-specific biomarkers such as amyloid-beta (Aβ), tau protein, and alpha-synuclein (α-Syn). The integration of these materials into advanced biosensors for real-time detection holds the promise of revolutionizing neurodiagnostic, enabling non-invasive, highly sensitive, and specific detection platforms. Furthermore, the incorporation of artificial intelligence (AI) and machine learning (ML) techniques into the analysis of sensor data enhances diagnostic accuracy and allows for more efficient interpretation of complex biomarker profiles. AI and ML can optimize feature selection, improve pattern recognition, and facilitate the prediction of disease progression, making them indispensable tools for personalized medicine. This review explores the potential of porous materials in neurodegenerative disease diagnostics, emphasizing their design, functionality, and the synergistic role of AI and ML in advancing clinical applications.