This article explores the integration of Neuro-PID technologies within the framework of IEC-61499 Function Blocks to enhance industrial automation systems, enabling decentralized control structures with enhanced interoperability. Neuro-PID combines neural network-based control and PID algorithms, introducing intelligence for dynamic adaptation based on real-time feedback. The case study focuses on replacing conventional PID controllers with neural network-based controllers in a series tank system, ensuring stable operation despite environmental disturbances. Function Blocks designed for the SIMATIC ET 200SP Open Controller PC2 facilitate seamless integration and control over fluid level, temperature, and flow, while shared memory enhances data acquisition and transfer between processes. The Neuro-PID Function Block predicts optimal PID constants based on dynamic process setpoints, ensuring adaptive control in response to changing system conditions. This integration, empowered by TensorFlow libraries and HDF5 model storage, represents a significant advancement towards intelligent and adaptable control systems in modern industrial environments.