Early detection of Alzheimer's disease (AD) is crucial for timely intervention and better patient outcomes. Machine learning (ML) techniques have shown promise in improving the accuracy and efficiency of AD diagnosis by analyzing complex data from various sources, including neuroimaging, genetic, and clinical data. This study provides a comparative analysis of different machine learning techniques for the detection of Alzheimer's disease, focusing on their strengths, weaknesses, and overall performance. We review widely-used ML algorithms such as Support Vector Machines (SVM), Decision Trees, Random Forests, k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Deep Learning approaches like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). We also discuss feature selection methods, evaluation metrics, and validation strategies commonly employed in AD detection research. Our analysis reveals that, while there is no one-size-fits-all solution, certain algorithms demonstrate superior performance in specific contexts or when combined as ensemble methods. This comprehensive comparison of ML techniques for Alzheimer's disease detection can serve as a guide for researchers and practitioners to select and optimize the most appropriate methods based on their specific needs and data characteristics, ultimately contributing to more accurate and timely diagnosis of AD.