Understanding human behavior in strategic interactions is crucial for various fields ranging from economics to artificial intelligence. Level-0 models, which assume minimal cognitive effort and use simple heuristics, provide a foundational framework for predicting human decisions in game theory. This paper explores the concept of Level-0 models and their application in analyzing human behavior in strategic games. Level-0 models hypothesize that individuals adopt straightforward decision-making strategies, often based on immediate incentives or basic rules of thumb. Despite their simplicity, these models can offer valuable insights into human behavior, particularly in settings where rationality assumptions may not fully apply. This study reviews prominent Level-0 models, such as random choice, imitation, and heuristic-based strategies, and evaluates their effectiveness in predicting behavior across different game scenarios. Emphasis is placed on how these models capture behavioral patterns that deviate from traditional rational choice theory, shedding light on the complexities of decision-making under uncertainty. Furthermore, the paper discusses practical implications and limitations of Level-0 models in various applications, including economics, psychology, and game theory. By bridging theoretical insights with empirical evidence, this research contributes to a deeper understanding of human behavior in strategic contexts and informs the development of predictive models in diverse domains.