Alzheimer’s Disease (AD) is the most prevalent form of dementia globally, which presents a pressing health issue, especially in aging populations. Its early detection is critical to initiating appropriate care and therapeutic strategies. However, AD’s complex and multifaceted nature poses considerable challenges to accurate and early diagnosis. Machine learning (ML) models have emerged as promising disease detection and diagnosis tools, including AD. However, despite their superior predictive performance, these models are often viewed as “black boxes” due to their complex internal workings, which are not readily interpretable. This study aims to explore the application of Explainable Artificial Intelligence (XAI) techniques to enhance the interpretability of the best-performing ML classifier for AD detection. The robust analysis offers significant insights into the ML model’s decision-making processes, thereby enhancing their interpretability and bolstering confidence in their use for early AD detection.