<p>This study seeks to improve early diagnosis of Alzheimer's disease (AD) through prediction models with high accuracy and low computational complexity for practical adoption. Deep learning (DL) based methods have proven to have outstanding predictive performance for medical data analysis, but their high computational requirements and complexity often make them unsuitable for large-scale adoption in clinical environments. To mitigate this, this study targets machine learning (ML) methods that achieve an optimal trade-off between performance and efficiency with low computation complexity. We undertook a thorough performance comparison and investigation of several predictive models against a well-annotated dataset of magnetic resonance imaging imaging and clinical data, with performance indicators including accuracy, mean squared error, and R-squared values. In this process, we critically evaluated and contrasted strengths and limitations of each model to establish which of these methods is most appropriate for early AD prediction. The study compares several ML methods, including gradient boosting, random forest, and linear regression, with the DL models, with a focus placed on those that can be implemented with minimal loss of accuracy in low-resource environments. Our results show that, although DL based models show high accuracy, their computational complexity renders them unsuitable for everyday clinical environments. On the other hand, lightweight ML models provide an ideal trade-off with accurate results and low computational requirements. This study not only bridges an essential gap for existing literature but presents a realistic solution for enhanced early diagnosis for the AD, leading to timely intervention and improved care for patients.</p>