Human Activity Recognition (HAR) has become a pivotal component in the development of intelligent environments, particularly within the context of smart homes. HAR systems enable devices to understand and respond to occupants’ behaviors, enhancing comfort, security, healthcare monitoring, and energy efficiency. Traditional HAR models often rely on either sensor-based or vision-based data, each with inherent limitations. Recent advances in Artificial Intelligence (AI), particularly hybrid models that integrate machine learning, deep learning, and rule-based systems, have significantly improved activity recognition accuracy and adaptability. This paper explores the foundational technologies behind HAR, focusing on hybrid AI architectures that combine various data modalities and learning paradigms. It discusses use cases such as elderly care, anomaly detection, personalized automation, and health monitoring. Case studies from real-world implementations are analyzed to demonstrate the practical benefits and outcomes of hybrid HAR systems. Ethical and regulatory considerations, including privacy, consent, and data security, are critically examined. The paper also outlines key challenges such as sensor noise, user variability, and real-time processing constraints. Finally, it explores future innovations in edge computing, federated learning, and context-aware reasoning that promise to further enhance HAR in smart living environments