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Introduction: The primary aim of this article is to critically assess and compare conventional diagnostic methods for Alzheimer's disease (AD), with a particular focus on the promising capabilities of biomarkers and brain mapping techniques. As the incidence of AD rises globally, novel diagnostic strategies are needed to improve upon traditional methods, which often lack predictive accuracy and precision. This study provides an in-depth review of advanced diagnostic tools, including Artificial Intelligence (AI) applications (e.g., machine learning and deep learning), brain mapping techniques (e.g., electroencephalography and Magnetic Resonance Imaging), and biomarkers (e.g., tau protein and beta-amyloid), which can be identified through innovative visual and manual techniques. Additionally, the research explores the potential of identifying precursor proteins in the blood of patients with AD before symptom onset, presenting a significant opportunity for early intervention that could greatly impact treatment outcomes. Conclusion: The findings underscore the potential of combining brain mapping methods with manual analysis to facilitate transformational advancements in the diagnostics of AD. This combined approach enhances the detection of structural and functional brain changes associated with AD, contributing to more accurate and earlier diagnoses. Furthermore, brain-derived proteins are present at significantly higher levels in cerebrospinal fluid than in blood, where they are diluted by abundant plasma proteins, such as albumin and immunoglobulins. This observation raises questions about the reliability of current clinical diagnostic practices and emphasizes the importance of validating new diagnostic markers with AI-based manual techniques against neuropathological standards. Finally, the study concludes that AI, when used in conjunction with cognitive assessments, biomarkers, brain mapping approaches, and molecular testing, can substantially enhance diagnostic accuracy and reliability, which are essential for managing and treating patients with AD effectively.