Physics-informed neural networks (PINNs) integrate physical principles into machine learning, enhancing predictive accuracy and solving complex problems governed by physical laws. This survey reviews PINN methodologies, applications, challenges, and future directions. PINNs combine data-driven learning with physics-based constraints, enabling efficient modeling of phenomena like fluid dynamics, material properties, and inverse problems. Key challenges include computational complexity, data scarcity, and integrating complex physical laws. Future research focuses on algorithmic improvements, interdisciplinary collaboration, scalability, and robustness. PINNs are applied in fluid dynamics, material science, quantum mechanics, and other fields, offering solutions to forward and inverse problems. They improve model accuracy, generalization, and interpretability, bridging the gap between data-driven and physics-based approaches. The survey highlights the potential of PINNs in advancing scientific and engineering applications through hybrid modeling.Physics-informed neural networks (PINNs) integrate physical principles into machine learning, enhancing predictive accuracy and solving complex problems governed by physical laws. This survey reviews PINN methodologies, applications, challenges, and future directions. PINNs combine data-driven learning with physics-based constraints, enabling efficient modeling of phenomena like fluid dynamics, material properties, and inverse problems. Key challenges include computational complexity, data scarcity, and integrating complex physical laws. Future research focuses on algorithmic improvements, interdisciplinary collaboration, scalability, and robustness. PINNs are applied in fluid dynamics, material science, quantum mechanics, and other fields, offering solutions to forward and inverse problems. They improve model accuracy, generalization, and interpretability, bridging the gap between data-driven and physics-based approaches. The survey highlights the potential of PINNs in advancing scientific and engineering applications through hybrid modeling.