Alzheimer's Disease (AD) has emerged as a major global public health challenge in the 21st century. Current clinical diagnosis primarily relies on techniques such as cerebrospinal fluid biomarker testing and amyloid PET imaging, yet these methods have exhibit significant limitations: cerebrospinal fluid testing is invasive, and PET imaging involves high costs and radiation exposure. Although neuropsychological scales are widely used in clinical practice, heir strong subjectivity and lack of specificity considerably reduce diagnostic sensitivity, particularly in early-stage AD. In response to this situation, this study provides a systematic review of the latest advances in non-invasive diagnostic technologies for AD, with a focus on breakthroughs in peripheral biofluid (e.g., blood, saliva) biomarker detection techniques, as well as the application prospects and innovative value of artificial intelligence-driven multimodal data integration strategies in the early identification and precise diagnosis of AD.