Innovative A timely and accurate diagnosis of Alzheimer's disease (AD) is fundamental to promptly begin treatment and intervention. Speech and writing patterns may be used to diagnose neurological disorders include Alzheimer’ as NLP has just recently arisen as a cognitive decline assessment technique. This study explores unique Natural Language Processing (NLP) methods that make use of ML and DL algorithms to more accurately as certain and classify AD in its early stages. This work offers a thorough analysis of many models that use linguistic data to categorize cognitive decline linked to Alzheimer's disease. These models include CNNs, RNNs, AEs, and Deep Belief Networks (DBNs). Furthermore, it explores the process of acquiring data, methods of preprocessing, and the significance of multimodality imaging in improving the accuracy of categorization. Furthermore, the paper tackles issues such as the restricted accessibility of extensive datasets and the intricacy of multimodal picture processing. These models may improve early identification rates and reduce misclassification errors, according to the research. Finally, it lays out future directions for cognitive bias evaluation based on NLP, such as developing more complex models and incorporating expanded and more heterogeneous datasets. The findings of this research suggest that advancements in DL and NLP have potential for the creation of dependable, cost-effective, non-invasive diagnostic tools for AD.