Artificial Intelligence (AI) has evolved significantly from its early days, with reasoning now being the critical component that distinguishes current systems from their predecessors. Unlike simple pattern recognition and prediction, reasoning enables AI to handle complex, real-world situations by making decisions akin to human cognition, such as inferring cause-and-effect relationships and generating hypotheses with incomplete information. Four main types of reasoning—deductive, inductive, abductive, and commonsense—each contribute uniquely to AI's ability to understand and interact with the world. Symbolic reasoning, neural networks, and hybrid models like neuro-symbolic AI illustrate different approaches to implementing reasoning in machines, each with its strengths and limitations. Large language models (LLMs) are at the forefront of this development, demonstrating emerging reasoning capabilities through benchmarks like GSM8K and MMLU, although they sometimes fall short in areas requiring commonsense logic. Real-world applications of AI reasoning span various industries, including healthcare, customer support, cybersecurity, and autonomous systems, where AI not only recognizes patterns but also assists in decision-making processes. The future points towards "reasoning-as-a-service," where hybrid models will likely dominate, making AI reasoning accessible and integral to enterprise systems.