This chapter investigates the use of Artificial Intelligence (AI) for climate-aware yield prediction for precision agriculture, focusing on the impact of climate change on crop yield. Precision agriculture, powered by things like sensors, satellite imagery, and AI, enables farmers to make data-oriented decisions that increase their crop yields, minimize their waste, and ensure they are operating sustainably. With more climate variables including temperature, precipitation, and extreme weather events being more and more affected by climate change, the need to consider the effect of climate factors in agricultural forecasting models is urgent. AI: Agricultural forecasting is being revolutionized by AI, applying ML and DL models to crunch huge amounts of data to make more accurate predictions of high-growth yields than traditionally used methods. In this research, we explore the potential of climate information as a part of the AI data in crop yield prediction models. We also investigate several AI models such as Random Forest, Support Vector Machines, Recurrent Neural Networks, and Convolutional Neural Networks, and their performance in climate-aware yield prediction. Furthermore, it discusses problems for adopting AI models and possible future research directions in the agriculture sector such as data quality, model interpretability and scalability, and possible solutions to overcome the challenges for AI models deployment. The chapter ends on an outlook and discusses what the future may hold for AI in agriculture, such as integrating real-time data collection, development in hybrid AI models, and the policy backing necessary to upscale AI-facilitated sustainable agriculture. The results suggest AI can greatly improve yield prediction by considering climate in a way that is robust to climate change, which is vital for food security and sustainable agriculture.