Detecting Alzheimer's Disease represents a pressing healthcare challenge, with current diagnostic methods often lacking in accessibility and efficiency. Leveraging advancements in machine learning and voice data analysis, our project aims to develop a novel approach for early detection of Alzheimer's Disease. By analyzing patterns in voice data, our model seeks to identify subtle markers indicative of cognitive decline, enabling timely intervention and improved patient outcomes. Our objective is to create a scalable and accurate tool that empowers healthcare professionals with an accessible and efficient method for Alzheimer's detection, ultimately contributing to early diagnosis and management of this debilitating neurological disorder.