B. Sree Charan , D. Bharani , P. Vishnuvardhan Reddy , P. Manasa 3 2 ( April - June ) The early detection of Alzheimers Disease AD is important to slow the diseases progression and manage patient care But conventional diagnosis techniques are often expensive invasive and not scalable for screening The proposed solution integrates handwriting recognition and speech recognition for early diagnosis of AD in a noninvasive way The approach employs a Random Forest model for handwriting features and a Convolutional Neural Network CNN for speech signals The two modalities are integrated by a decisionlevel fusion scheme The findings indicate that the developed model has an overall accuracy of 968 surpassing the accuracy of the individual models and has good robustness for practical screening Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026