The healthcare system for elderly people faces several challenges, which can be addressed using advanced machine learning models. These models can help monitor chronic diseases, detect falls, and provide personalized health recommendations. The study uses comprehensive datasets like MIMIC-III/IV, WESAD, and UCI HAR to explore human movements, device limitations, and the differences in fall occurrences. A detailed review of existing literature discusses current technologies for activity monitoring and fall detection, focusing on deep learning methods like convolutional neural networks (CNNs) for detecting unusual patterns, recurrent neural networks (RNNs) and long short-term memorys (LSTMs) models for analyzing sequences of data, and deep reinforcement learning (DRL) for optimizing personalized treatment. The proposed solution uses federated learning, which helps protect patient privacy in healthcare applications. The models are tested and evaluated based on metrics such as accuracy, precision, recall, and F1-score, showing both their strengths and weaknesses. A comparison of different models provides valuable insights into their performance and relevance in clinical settings. The findings highlight how these technologies could improve outcomes for patients in critical care, with future research aimed at making the models more accurate and widely applicable for elderly health management.