Hybrid Artificial Intelligence refers to the integration of multiple AI paradigms within a single system in order to leverage the strengths of each approach while minimizing their individual limitations, typically combining symbolic rule-based reasoning and knowledge representation with data-driven machine learning and deep learning models based on artificial neural networks, and this approach emerged as a response to the shortcomings observed when relying solely on either symbolic or sub-symbolic methods, as symbolic systems provide high interpretability, logical consistency, and traceable decision pathways but struggle with scalability and unstructured large-scale data, whereas deep learning models excel at identifying complex nonlinear patterns in images, text, and biomedical signals yet often function as opaque black boxes with limited explainability and controllability, therefore hybrid systems aim to balance predictive performance with transparency and structured reasoning, and they are widely applied in domains such as precision medical diagnosis, bioinformatics analysis, industrial expert systems, adaptive robotics, cybersecurity frameworks, and strategic decision support systems, enhancing reliability by integrating logical inference, statistical learning, and continuous adaptation mechanisms, their architectures may follow sequential layered designs where one model refines or constrains the output of another, or parallel modular frameworks where multiple components collaborate before final decision aggregation, making them particularly valuable in high-stakes environments that demand both accuracy and regulatory compliance such as healthcare, finance, and legal analytics, moreover hybrid AI supports continual learning while preserving structured knowledge bases,