The global population is expected to reach 9.7 billion by 2050, requiring a 50% increase in food production. Climate change presents new challenges for agriculture, including extreme weather, rising temperatures, and precipitation changes. Artificial intelligence (AI) can help address these challenges by improving efficiency and productivity through monitoring crops and livestock, optimizing irrigation, predicting pests and diseases, and developing resistant crop varieties. This paper reviews the applications and challenges of AI in precision agriculture. AI-based technologies, such as machine learning algorithms and predictive models, can improve climate-smart agriculture by analyzing large volumes of climate, soil, and crop-related data. These algorithms generate accurate predictions and recommendations for optimizing farming practices, including precision irrigation scheduling, nutrient management, pest and disease monitoring, and yield forecasting. AI also contributes to resource efficiency by optimizing input usage, minimizing waste, and reducing environmental impact. The paper highlights the potential of AI to drive efficiency and productivity in climate-smart agriculture, despite challenges such as data quality, availability, technical expertise, and cost implications. By leveraging AI's capabilities, agriculture can move towards sustainable and resilient practices, achieving food security, enhancing resource efficiency, and mitigating climate change impacts.