The continuous monitoring of patients affected by Alzheimer’s disease requires autonomous and reliable machine-based systems capable of operating under strict energy, size, and safety constraints. This work proposes an intelligent mechatronic architecture for an implantable monitoring device integrating embedded biomedical sensors, low-power processing units, secure wireless communication, and artificial intelligence for real-time data analysis. To address the limited availability of clinical datasets, a digital twin-based synthetic data generation framework is developed. The proposed system is evaluated on a multidimensional dataset composed of 10, 235 records, including physiological, behavioral, and cognitive parameters, with an 80/20 train-test split. Random Forest, Support Vector Machine, and Deep Neural Network models are implemented and compared using standardized classification and regression metrics, including accuracy, precision, recall, F1-score, confusion matrices, and error-based indicators. The experimental results show that the Deep Neural Network consistently outperforms classical machine learning models, achieving higher classification accuracy, reduced misclassification rates, and more stable convergence behavior, as confirmed by learning and loss curves. From a mechatronics perspective, the proposed solution emphasizes modular system integration, computational efficiency, and compatibility with implantable hardware constraints. The results demonstrate the feasibility of embedding intelligent decision-making capabilities into compact mechatronic systems, highlighting their relevance for intelligent machines and continuous monitoring applications.