<p>Dementia is a major public health issue, characterized by declining cognitive functions, high mortality rates, and significant costs associated with diagnosis, treatment, and care. While the condition is currently incurable, early diagnosis allowed for critical support, targeted medication, and optimal participation in intellectual, social, and physical activities. This leads to a better life quality for both patients and their families, with early detection of Alzheimer’s Disease being especially important. This survey paper explores various techniques used for Alzheimer’s Disease detection, emphasizing a two-step approach: achieving accurate classification and providing clear justifications for clinicians. It examines a range of established and emerging methods, including neuroimaging analysis using MRI and PET scans, cognitive and behavioral assessments to identify decline, and blood-based biomarkers associated with Alzheimer’s Disease development. Additionally, the survey delves into the role of Explainable Artificial Intelligence in making these algorithms more transparent and trustworthy for medical professionals. Alzheimer’s disease faces challenges like limited data availability and data imbalance, which can bias models. Finally, this review covers available databases and strategies to address these data issues.</p>