Artificial intelligence approaches such as graph neural networks, transformers, probabilistic models, and hybrid physics‑machine learning systems are increasingly used to improve weather and climate prediction. These methods complement or replace traditional numerical weather prediction that relies on solving physics equations on supercomputers. By learning patterns from data and integrating physical constraints, AI enables faster, more accurate environmental intelligence.