Alzheimer’s disease (AD) is a brain disorder and usual form of dementia, which constitutes almost 75% among all the dementia cases. Further, Alzheimer’s disease is considered as the major burden to the worldwide healthcare system, since it is expected to affect millions of people in the upcoming years. However, Alzheimer’s disease still remains incurable, due to its multi-factorial nature of symptoms. Due to these reasons, early-stage diagnosis of Alzheimer’s disease is essential, which helps in treatment and recovery of patients to a greater extent. Though recently popular Machine Learning techniques like SVM are successfully employed in predicting AD, most of the existing approaches are not fully focused on aspects like speeding-up of training process, increasing robustness and optimizing model parameters. To solve these issues, this article presents an Efficient Light Gradient Boosting Machine (LGBM)-based framework, for the early-stage detection of Alzheimer’s disease. The experiments conducted using the real-world MRI datasets of patients clearly demonstrate the better performance of the proposed work in terms of prediction metrics compared to the existing techniques.