The operational complexity of modern industrial processes demands control frameworks that are both mathematically rigorous and computationally agile. This paper provides a systematic review of the transformative advances in Nonlinear Model Predictive Control (NMPC) integrated with Deep Learning (DL) methodologies between 2020 and 2026. We categorize the state-of-the-art into three technical pillars: neural-based system identification, computational acceleration via latent-space optimization, and robust architectures for uncertain environments. By analyzing applications across chemical engineering, fusion energy maintenance, and bionic robotics, we evaluate how hybrid frameworks-such as LSTM-based estimators and autoencoder-driven reduced-order models-mitigate the traditional trade-offs between model fidelity and real-time feasibility. The review further discusses emerging trends in cyber-secure control via homomorphic encryption and the integration of Physics-Informed Neural Networks (PINNs). Our synthesis highlights persistent gaps in formal stability proofs and model interpretability, offering a strategic roadmap for future research in autonomous industrial intelligence.
Tuyboyov Oybek (Fri,) studied this question.