The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has given rise to the Internet of Behaviors (IoB) paradigm. This emerging concept leverages IoT sensors, big data analytics, and AI advancements to gather, analyze, and interpret human behavioral data. This review aims to provide a taxonomy and analysis of the current state and trajectory of IoB. We categorize IoB approaches into online and physical variants, analyzing their key characteristics, data sources, privacy considerations, and monitoring capabilities. Through a structured literature survey, we explore the applications and research challenges through several domains such as healthcare, marketing, transportation, and manufacturing. The comparative analysis conducted in this study offers insights into the complementary strengths and limitations of online and physical IoB, providing a clear definition of these concepts and a taxonomy of their key characteristics. Based on these definitions, this study investigates their applications across diverse sectors and identifies critical criteria (data sources, privacy concerns, ethical considerations, and implementation challenges) to provide a synthetic overview of their contribution to IoB development.