Typically, real-time Cyber-Physical Systems (CPS) are increasingly requiring real-time responsive hardware that is flexible and adaptable to the ever-changing environment and operational needs. In FPGA traditional configurations, despite their ability to allow reconfigurability, these designs tend to be very slow, consume large amounts of power, and are not very flexible due to the requirement for full-bitstream reconfigurability or manual triggering. Such constraints are a drawback to CPS applications, such as autonomous vehicles, industrial automation, and smart grids, where hardware adaptation is crucial for performance, reliability, and fault tolerance in real-world conditions that are unpredictable. To overcome these difficulties, this paper presents an Intelligent Bitstream Reconfiguration Framework that utilises the partial reconfiguration features of FPGAs in conjunction with an artificial intelligence-based decision engine. The system constantly monitors sensor data and performance metrics, using machine learning models to predict workload fluctuations and potential faults. When the framework identifies suboptimal conditions, it triggers specific partial bitstream updates, which minimise downtime and power overhead. To enhance security and integrity, the method incorporates cryptographic validation to verify the integrity of the bitstream before deployment, thereby preventing malicious modification. The simulation outcome shows that the proposed solution decreases reconfiguration latency by up to 65%, power consumption by 30%, and fault recovery time by 40% compared to conventional methods. Additionally, the decision engine accurately predicts the optimal reconfiguration points, demonstrating its capability to reduce redundant updates and conserve resources in the system, with an accuracy rate of more than 90%. This is an intelligent, context-aware mechanism that enables CPS hardware to be self-adapted effectively and safely in real-time, thereby significantly improving system resilience and operational efficiency. The model preconditions stronger and more sustainable CPS implementations that will be able to endure the strict requirements of contemporary dynamic settings.