The disease course of Alzheimer’s disease (AD) results in severe brain degeneration which devastates both mental capacities and life quality. The proper detection of Alzheimer's disease at its early stage serves as an essential requirement for successful intervention strategies. Standard diagnostic procedures such as neuroimaging and cognitive screening methods require long duration and high costs but frequently reveal the disease after it reaches its later stages. The combination of artificial intelligence technologies and biosensors has developed into an effective method that enables real-time detection of Alzheimer's disease. AI models enable the analysis of complex biosensor data, resulting in the discovery of subtle biomarkers that signal early-stage AD, allowing for quick diagnosis and personalized medicinal regimens. Biosensor and AI development combine to create a system for immediate AD surveillance through trained device signals alongside EEG data, blood-related indicators, and gait pattern analysis. The comparison of two AD detection models uses support vector machines coupled with deep learning convolutional neural networks to establish their detection metrics which include accuracy levels sensitivity rates and precision measurements. Results show that the CNN approach reaches better accuracy rates compared to standard methods when classifying AD. The system design supports rapid non-invasive cost-effective screening procedures that are primed for implementation at large clinical settings. AI-powered biosensor integration enables early AD detection, opening new options for enhanced patient management and optimal intervention chances.