Alzheimer’s disease presents a significant global health challenge, highlighting the need for early detection to enable timely intervention and improve treatment outcomes. This study analyzes handwriting data from individuals with and without Alzheimer’s to identify key predictive features across various tasks. While machine learning models have been widely applied to Alzheimer’s prediction, their black-box nature demands greater explainability for informed decision-making. We employed explainable ensemble models, including Random Forest, Bagging, XGBoost, LightGBM, AdaBoost, and gradient boosting, to classify Alzheimer’s patients based on handwriting tasks. The analysis covered task categories such as copying, graphic, and memory-based activities, offering insights into handwriting’s role in Alzheimer’s prediction. To enhance model interpretability, we applied SHapley Additive exPlanations (SHAP) to reveal the influence of specific features on predictions. Our findings indicate that time-related features were consistently significant, particularly in copying and graphic tasks, as they reflect cognitive processing time. Pressure-related features were important in memory tasks, potentially indicating confidence in recalling information. Additionally, simpler graphic tasks demonstrated greater discriminative power, suggesting their potential for detecting early cognitive decline.