A reasoning engine is a critical component in next-generation AI systems that mimics human-like problem-solving by applying logical rules and structured knowledge to derive conclusions, make decisions, and solve tasks. Unlike inference engines that apply learned patterns or search engines that locate information, reasoning engines interpret context, evaluate hypotheses, and apply logic to deduce new knowledge. These engines are essential in improving the explainability, consistency, and safety of AI applications across various domains, including healthcare, regulatory compliance, and customer service. Reasoning engines typically consist of several modular components, such as a knowledge base, inference engine, working memory, user interface, explanation module, and integration layer, to perform tasks involving different reasoning types like deductive, inductive, abductive, and more. Modern reasoning engines increasingly combine symbolic and neural methods to enhance adaptability while maintaining interpretability, and they are becoming prevalent in diverse industries by transforming how AI systems handle complex workflows, reduce costs, and improve efficiency and trust. As AI evolves, the integration of reasoning engines with machine learning models and large language models is seen as a promising approach to enable more comprehensive and reliable AI agents capable of executing multi-step tasks and making autonomous decisions.