Autonomous systems rely on reasoning models to interpret sensory inputs, evaluate possible actions, and choose the most appropriate course based on predefined goals or learned experiences. These models are categorized into three principal types: symbolic reasoning models, probabilistic reasoning models, and logic-based models. Symbolic models operate on explicitly defined symbols and rules, while probabilistic models handle uncertainty and incomplete information. Logic-based models expand on symbolic reasoning by incorporating temporal and event-based logic to handle reasoning about actions and changes over time. Cognitive architectures aim to simulate human-like decision-making capabilities by integrating multiple reasoning paradigms. Hybrid models combine different types of reasoning to leverage the strengths of each, while emerging reasoning models such as neuro-symbolic reasoning and causal reasoning aim to bridge the gap between low-level data processing and high-level decision-making. Machine learning-based reasoning is becoming a foundational mechanism in modern autonomous systems, enabling capabilities such as knowledge acquisition, inference, planning, and contextual adaptation. Real-world applications of reasoning models span multiple domains, including autonomous vehicles, industrial robotics, healthcare systems, enterprise knowledge management, virtual assistants, and customer support bots. However, implementing these models presents several technical and practical challenges, including scalability, data demands, transparency, integration complexity, ethical encoding, generalization, and cross-disciplinary coordination. Overcoming these challenges requires multidisciplinary strategies, investment in explainable AI, continuous evaluation frameworks, and adherence to ethical and legal standards.