The landscape of meteorological modeling is shifting from purely numerical weather prediction (NWP) to integrated systems combining physics-based models with machine learning. While traditional NWP relies on solving mathematical equations for atmospheric variables—a process that is computationally expensive due to high resolution requirements—machine learning approaches learn atmospheric dynamics directly from historical datasets like ERA5. Key architectures driving this change include Graph Neural Networks (GNNs), which model spatial dependencies by treating geographical regions as nodes in a graph, and Transformers, which capture complex spatio-temporal relationships. Notable implementations include Google DeepMind's GraphCast, which demonstrates efficient global forecasting, and GenCast, which utilizes generative AI for probabilistic ensemble forecasting to quantify uncertainty. Furthermore, the European Centre for Medium-Range Weather Forecasts (ECMWF) has moved AI into operational meteorology with its Artificial Intelligence Forecasting System (AIFS) v2, introduced in May 2026. Rather than replacing physics, the field is moving toward hybrid models like NeuralGCM, which integrate machine learning approximations within physical frameworks to address the "out-of-distribution" problem—where models struggle to predict unprecedented atmospheric states not present in training data. This evolution aims to create "environmental intelligence" by combining remote sensing, data assimilation, and scalable neural architectures.