This session explores the application of artificial intelligence and physics-informed machine learning to production forecasting, optimization, and proactive well operations. Topics span physics-constrained neural networks for multi-horizon decline curve analysis, large language models for gas network optimization, transformer and diffusion-based anomaly detection, and PINN-driven equation discovery for unconventional reservoirs. The session also addresses intelligent early warning systems for stuck-pipe incidents, distributed acoustic sensing for flow allocation, and surrogate-assisted history matching and uncertainty quantification. Together, these contributions highlight the industry's progression from reactive monitoring toward predictive, physics-aware decision-making across the full production lifecycle.