Weather and Climate: Applications of Machine Learning and Artificial Intelligence offers a thorough and accessible introduction to how machine learning is transforming weather forecasting and climate research. The book opens by detailing the core principles and statistical methods behind machine learning, giving readers a solid foundation before moving into an informative survey of model types. Readers gain insight into how emulation and sub-grid scale parameterizations are revolutionizing computational approaches, while practical examples show how artificial intelligence and machine learning are being integrated with traditional forecasting techniques. Key chapters also address the importance of explainable AI, helping users interpret results and model behaviors. The book also investigates generative AI's emerging role in atmospheric science and examines the convergence of data assimilation with machine learning for improved forecasting accuracy. Case studies highlight real-world applications in environmental monitoring, bridging theory and practice. Concluding with a forward-looking discussion, the authors consider the future impact of AI and machine learning on climate science, making this guide an essential resource for researchers and practitioners eager to explore new frontiers in weather and climate analytics.