Alzheimer’s Disease (AD) is a type of dementia that occurs in patients with increasing severity of deficits in cognitive abilities, memory, and nutritional status, and shifts in behavior. There is virtue in early diagnosis, and this should be compounded with accurate diagnosis. Clinical evaluation and imaging studies represent common diagnostic approaches and they are used and might be inaccurate and require a lot of time. More specific innovations in modern machine learning (ML) and biosensor technology suggest suitable approaches to build less subjective and more sensitive diagnostics. This study examines how biomarker-derived variables can be implemented into ML algorithms that are trained from datasets collected from the integrated biosensor platforms to detect AD. Concerning such biomarkers, we explore how well various ML models can classify AD patients from healthy controls. Further, the potential of optical fiber sensors to improve the sensitivity and specificity of biosensing platforms is also discussed in the study. The use of these technologies has been envisioned in complementing existing approaches in AD diagnosis to enhance its efficiency, thus early intervention, and better results for the patients. Alzheimer’s Detection using Machine Learning and Biosensor Integration.