This study explores the potential of artificial intelligence (AI), particularly machine learning and deep learning, to develop climate adaptation strategies that enhance agricultural productivity. By integrating AI with climatological data, the research aims to predict and mitigate the adverse impacts of climate change on crop yields. The study uses historical climate data, including temperature, rainfall, soil moisture, and crop genetic information, to train machine learning and deep learning models. These models are effective in forecasting future agricultural outcomes under various climatic scenarios and suggest optimal adaptation strategies to improve crop yields. The findings highlight the efficacy of AI in transforming data into actionable insights, enhancing agricultural productivity, and contributing to sustainable agricultural practices. The research also paves the way for future studies on integrating AI with real-time environmental sensing technologies, offering a dynamic framework for sustainable farming practices and global food security in the face of climate challenges. The study's practical application in real-world agricultural settings demonstrates the effectiveness of AI-driven strategies in improving crop yields and water efficiency, despite challenges such as initial resistance from farmers and logistical hurdles. Overall, the research underscores the transformative potential of AI in agriculture, emphasizing the need for continuous support and capacity-building efforts to ensure equitable access to these technologies.This study explores the potential of artificial intelligence (AI), particularly machine learning and deep learning, to develop climate adaptation strategies that enhance agricultural productivity. By integrating AI with climatological data, the research aims to predict and mitigate the adverse impacts of climate change on crop yields. The study uses historical climate data, including temperature, rainfall, soil moisture, and crop genetic information, to train machine learning and deep learning models. These models are effective in forecasting future agricultural outcomes under various climatic scenarios and suggest optimal adaptation strategies to improve crop yields. The findings highlight the efficacy of AI in transforming data into actionable insights, enhancing agricultural productivity, and contributing to sustainable agricultural practices. The research also paves the way for future studies on integrating AI with real-time environmental sensing technologies, offering a dynamic framework for sustainable farming practices and global food security in the face of climate challenges. The study's practical application in real-world agricultural settings demonstrates the effectiveness of AI-driven strategies in improving crop yields and water efficiency, despite challenges such as initial resistance from farmers and logistical hurdles. Overall, the research underscores the transformative potential of AI in agriculture, emphasizing the need for continuous support and capacity-building efforts to ensure equitable access to these technologies.