Alzheimer’s disease (AD) is the leading cause of dementia, responsible for 60-70% of cases and currently affecting around 55 million people globally. As the global population ages, this number is expected to reach 152 million by 2050. Early diagnosis is key to managing AD, as it allows for treatments that can alleviate symptoms and improve the quality of life for those in the early stages of the disease. Traditional diagnostic methods focus on clinical observations, such as changes in the hippocampus, a brain region heavily impacted by AD. However, these methods often fail to detect the disease in its earliest stages due to a lack of specific biomarkers and inconsistent diagnostic criteria. Recent advancements in technology, particularly in Artificial Intelligence (AI), offer new hope for early diagnosis. Researchers have developed a predictive prognostic model (PPM) that uses AI to assess the likelihood and speed at which individuals with mild cognitive impairment (MCI) or even those who are cognitively normal may develop AD. This model has shown high accuracy and reliability, surpassing traditional diagnostic methods. By integrating AI, this approach enhances the precision of early detection, enabling more timely intervention and improving patient outcomes. This marks a significant advancement in the fight against Alzheimer’s disease.