Digital twins are emerging as powerful tools for real-time simulation, monitoring, and predictive control of oceanic and atmospheric systems. Creating effective digital twins for such complex environments requires computational efficiency without sacrificing interpretability and accuracy. This mini-symposium focuses on advanced Reduced Order Models (ROMs) and hybrid, physics-informed machine learning approaches, offering real- time performance while preserving the critical physical characteristics of environmental processes. Oceanic and atmospheric phenomena, characterized by multiscale and multiphysics interactions, pose substantial computational challenges, particularly for high-resolution and real-time applications. Hybrid approaches leveraging physics-informed neural networks (PINNs), dynamic mode decomposition (DMD), proper orthogonal decomposition (POD), and generative AI techniques bridge the gap between computational feasibility and physical fidelity. These methods embed domain-specific knowledge into data-driven frameworks, enhancing reliability, interpretability, and predictive capabilities of digital twins. Target applications of interest include digital twins for offshore energy systems (e.g., wind farms, wave energy converters, oil/gas platforms), aerosol-cloud interactions in climate modeling, ocean-atmosphere coupling dynamics (e.g., hurricane forecasting, air-sea interactions), and atmospheric pollution transport. Contributions highlighting advancements in multi-fidelity modeling, data assimilation techniques, uncertainty quantification, and computational efficiency improvements are particularly encouraged. Aligned closely with WCCM-ECCOMAS 2026 themes—Digital Twins (700), Multiscale and Multiphysics Systems (1600), and Scientific Computing (1800)—this symposium will foster interdisciplinary exchange among researchers from computational mechanics, environmental engineering, climate science, and applied mathematics communities. Participants will discuss recent developments, challenges, and future directions to advance real-time, scalable, and interpretable digital twins for oceanic and atmospheric systems.