This paper introduces NeuroPhysNet, a novel hybrid neural network architecture designed for enhanced prediction and control of cyber-physical systems (CPS). The increasing complexity and dynamic nature of CPS present significant challenges in modeling and control, often stretching the limits of traditional approaches. NeuroPhysNet addresses these challenges by seamlessly integrating deep learning techniques with physics-based modeling, offering a robust framework that leverages both data-driven insights and domain-specific knowledge. The architecture comprises three main components: a Deep Neural Network (DNN) module for capturing complex non-linear relationships, a Physical Model Integration (PMI) module for incorporating domain-specific equations, and an Adaptive Fusion (AF) module for dynamically combining outputs based on current system states. We evaluate NeuroPhysNet across multiple CPS domains, including smart grid energy management, autonomous vehicle control, and industrial process control. Results demonstrate significant improvements in prediction accuracy, physical consistency, and generalization capabilities compared to state-of-the-art baseline models. NeuroPhysNet achieves up to 15% improvement in mean squared error and a 20% increase in physical consistency scores across tested domains. This research contributes to the growing field of physics-informed machine learning, offering a versatile approach for enhancing the performance and reliability of complex cyber-physical systems.