This thesis addresses the critical challenges at the intersection of the Internet of Things (IoT) and healthcare, focusing primarily on innovative solutions for fall detection and data privacy. The research begins by underscoring the urgent need for robust, secure fall detection systems, which sets the tone and motivation for the ensuing chapters. A comprehensive survey of the existing literature is provided, encompassing key technologies like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) that serve as the theoretical foundation for the research. The core contribution is a novel Falls Management Framework (FMF) that employs a fusion of wearable and non-wearable sensors for effective fall detection. The framework utilizes various machine learning algorithms, with special emphasis on our proprietary Adaptive Context-aware Fall Detection Algorithm (ACFDA), optimized for minimizing false negatives and positives. In addition to FMF, the thesis explores innovative technologies in existing systems like SmartFall and FallRisk, and presents an advanced fall detection system based on visual object recognition algorithms. This latter system offers a comfortable, non-intrusive alternative to wearable sensors by using environmental sensors and real-time video analysis. Furthermore, the thesis addresses the significant issue of false detection rates and introduces privacy- preserving methods such as skeletal pose imaging and visual encryption techniques. This multi-layered approach aims to harmonize effective fall detection with individual privacy concerns. The findings and contributions of this research not only advance the field of IoT-based healthcare solutions but also promise to have immediate practical applications, especially for the vulnerable elderly population.