The memory and cognitive abilities of older people are significantly impacted by Alzheimer’s disease. Early and accurate identification is critical to improving patient care and enabling timely intervention. Electroencephalogram (EEG) signals are the most effective method of identification. Alzheimer’s disease has been identified using a variety of machine learning and deep learning techniques. These algorithms, including Decision Tree, Random Forest, Support Vector Machine, Convolutional Neural Networks, Deep Belief Networks, ResNet50, and Light Gradient Boosting Machine (LightGBM) models, have been thoroughly examined by us. Experimental results show that LightGBM outperforms the other models in terms of classification accuracy. These findings suggest that LightGBM offers an effective, non-invasive framework for real-time Alzheimer’s disease detection.