Alzheimer’s disease requires early detection for treatment and management but conventional tests are slow and less accurate. This chapter focuses on adversarial approaches, especially GANs, arguing for their usefulness in enhancing the early detection of Alzheimer’s disease. This influence increases diagnostic accuracy through artificially generated data, enlarged training sets, and looking for small biomarkers in the medical and neurological visuals and data. This chapter gives a background to Alzheimer’s disease and the general difficulties that limit the disease’s early detection and then we discuss in detail how the use of an adversarial method increases the sensitivity and specificity of the test. It also specifies an implementation framework for how such data pre-processing will be done, how the model will be trained, and how it will be integrated into clinical workflows. The case studies show that adversarial approaches can outperform other methods and solve such limitations by example while preserving ethical, privacy, and computational considerations. Last but not least, the chapter offers conversations on directions for further research and innovation taking a cue from adversarial techniques for diagnosing Alzheimer’s and possibly in other areas. This work was designed to narrow the gap between the emergence of new AI tools and practice in neurological healthcare by presenting a route towards increasing precision, feasibility, and accessibility of diagnostic and therapeutic options.