With the potential to improve outcome prediction, machine learning algorithms have been applied to detect (and potentially forecast) Alzheimer’s disease using genetic data. Still In its early stages, however, is the thorough investigation into the Analysis and detection of Alzheimer’s disease by genetic data. This study evaluated the scientific literature on the application of different machine learning techniques for the prediction of Alzheimer’s disease is based only on genetic information. The groundwork for a larger research plan cenered on creating innovative machine learning-based predictive algorithms for Alzheimer’s disease, to pinpoint gaps in the literature, and to critically evaluate the reporting and algorithmic techniques. The high risk of bias in the analysis can be demonstrated by The primary findings connected to techniques for validation, hyper-parameter searching, and feature selection.