Alzheimer’s disease (AD) is a prevalent neurological disorder with a significant global impact. Physicians conventionally use standard methods to diagnose AD, primarily relying on clinical assessments and professional judgment, along with a patient’s medical history, to identify early symptoms or diagnose present AD. AD diagnostics have been strengthened with technological advances over time, such as neuroimaging and biomarker analysis. However, despite their advantages, these techniques face limitations in processing extensive radiological brain data. Artificial Intelligence (AI) offers unparalleled potential for early and precise diagnosis of Alzheimer’s. Firstly, conventional methods for early AD detection are investigated, concentrating on their accessibility. Secondly, this review highlights novel AI-driven approaches that leverage machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for diagnostic purposes. This review highlights the potential of these tools in transforming Alzheimer’s early detection on a larger scale.