This multicenter, retrospective analysis examined how artificial intelligence can address structural and methodological barriers in Chinese epidemiological cohort studies, using deep learning, natural language processing (NLP), and federated learning models to enhance data integration, standardization, and longitudinal tracking. Researchers reviewed nationwide digital health initiatives, large-scale cohort platforms such as the China Kadoorie Biobank, and AI-driven data infrastructure projects conducted between 2004 and 2024. Preprocessing involved the development of automated data collection tools using optical character recognition and NLP, validated on more than 4,000 patient records with 98.66% accuracy and a 17-fold time reduction compared to manual collection. Models tested included deep neural networks, large language models (LLMs) such as DeepSeek, and federated learning frameworks, benchmarked against traditional cohort methods. The best-performing systems achieved substantial improvements in data completeness, analytic efficiency, and accuracy across hospital-based studies. However, there were some limitations. AI adoption remains concentrated in urban centers, data collection is non-standardized across regions, and rural primary healthcare facilities often lack technical infrastructure and trained personnel. Regulatory frameworks for AI validation and fairness analysis also remain incomplete, potentially reinforcing rather than reducing cohort representation biases.