Alzheimer's disease (AD) is the most common cause of dementia, affecting millions globally and posing significant economic and social burdens. It is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. AD is driven by the accumulation of amyloid beta (Aβ) plaques and neurofibrillary tangles, along with factors such as aging, genetics, and environmental influences. The disease involves complex mechanisms including Aβ aggregation, tau hyperphosphorylation, neuroinflammation, oxidative stress, mitochondrial dysfunction, and vascular issues. These mechanisms contribute to neuronal damage and cognitive decline. Diagnosis of AD involves cognitive tests, blood and CSF biomarkers, neurological exams, and brain imaging. CSF biomarkers like Aβ42/Aβ40 and p-tau are key for early detection, though blood-based biomarkers are being developed as non-invasive alternatives. Neuroimaging techniques such as MRI, PET, and SPECT provide insights into brain structure and function, aiding in diagnosis and monitoring disease progression. Therapeutic strategies for AD include both pharmacological and non-pharmacological approaches. FDA-approved pharmacological treatments include cholinesterase inhibitors and NMDA receptor antagonists, which help manage symptoms. Novel therapies target Aβ and tau pathology, neuroinflammation, and synaptic dysfunction. Monoclonal antibodies like aducanumab and lecanemab have shown promise in reducing Aβ plaques and improving cognition. Other emerging therapies include immunotherapy, gene therapy, and lifestyle modifications. Artificial intelligence and machine learning are increasingly used in AD diagnosis and treatment, analyzing complex data to improve accuracy and efficiency. These technologies help in early detection, personalized treatment, and monitoring disease progression. Overall, understanding AD pathogenesis, improving diagnostics, and developing effective therapies are crucial for addressing this urgent medical need.Alzheimer's disease (AD) is the most common cause of dementia, affecting millions globally and posing significant economic and social burdens. It is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. AD is driven by the accumulation of amyloid beta (Aβ) plaques and neurofibrillary tangles, along with factors such as aging, genetics, and environmental influences. The disease involves complex mechanisms including Aβ aggregation, tau hyperphosphorylation, neuroinflammation, oxidative stress, mitochondrial dysfunction, and vascular issues. These mechanisms contribute to neuronal damage and cognitive decline. Diagnosis of AD involves cognitive tests, blood and CSF biomarkers, neurological exams, and brain imaging. CSF biomarkers like Aβ42/Aβ40 and p-tau are key for early detection, though blood-based biomarkers are being developed as non-invasive alternatives. Neuroimaging techniques such as MRI, PET, and SPECT provide insights into brain structure and function, aiding in diagnosis and monitoring disease progression. Therapeutic strategies for AD include both pharmacological and non-pharmacological approaches. FDA-approved pharmacological treatments include cholinesterase inhibitors and NMDA receptor antagonists, which help manage symptoms. Novel therapies target Aβ and tau pathology, neuroinflammation, and synaptic dysfunction. Monoclonal antibodies like aducanumab and lecanemab have shown promise in reducing Aβ plaques and improving cognition. Other emerging therapies include immunotherapy, gene therapy, and lifestyle modifications. Artificial intelligence and machine learning are increasingly used in AD diagnosis and treatment, analyzing complex data to improve accuracy and efficiency. These technologies help in early detection, personalized treatment, and monitoring disease progression. Overall, understanding AD pathogenesis, improving diagnostics, and developing effective therapies are crucial for addressing this urgent medical need.