This study explores the potential of artificial intelligence (AI) in optimizing agricultural yields through climate adaptation strategies. As climate change threatens agricultural productivity with unpredictable weather and environmental changes, AI-based solutions are crucial for enhancing resilience and improving crop yields. The research integrates machine learning (ML) and deep learning (DL) techniques to analyze historical climate data and crop performance, enabling the development of adaptive strategies that predict and mitigate climate impacts on agriculture. AI models, trained on data including temperature, rainfall, soil moisture, and crop genetics, can forecast future agricultural outcomes under various climatic scenarios and suggest optimal adaptation strategies. These models provide actionable insights for farmers and policymakers, helping them make informed decisions aligned with anticipated climatic conditions. The study demonstrates that AI can transform data into practical insights, contributing to sustainable agricultural practices and global food security. The research also highlights the integration of AI with real-time environmental sensing technologies, offering a dynamic framework for agricultural management. While challenges such as data accessibility, model adaptability, and technology adoption remain, the findings underscore the transformative potential of AI in agriculture. The implementation of AI-based strategies in real-world settings has shown significant improvements in crop yields and water efficiency, demonstrating the practical viability of these technologies. The study concludes that AI has the potential to revolutionize agriculture, enhancing sustainability and productivity in the face of climate challenges.This study explores the potential of artificial intelligence (AI) in optimizing agricultural yields through climate adaptation strategies. As climate change threatens agricultural productivity with unpredictable weather and environmental changes, AI-based solutions are crucial for enhancing resilience and improving crop yields. The research integrates machine learning (ML) and deep learning (DL) techniques to analyze historical climate data and crop performance, enabling the development of adaptive strategies that predict and mitigate climate impacts on agriculture. AI models, trained on data including temperature, rainfall, soil moisture, and crop genetics, can forecast future agricultural outcomes under various climatic scenarios and suggest optimal adaptation strategies. These models provide actionable insights for farmers and policymakers, helping them make informed decisions aligned with anticipated climatic conditions. The study demonstrates that AI can transform data into practical insights, contributing to sustainable agricultural practices and global food security. The research also highlights the integration of AI with real-time environmental sensing technologies, offering a dynamic framework for agricultural management. While challenges such as data accessibility, model adaptability, and technology adoption remain, the findings underscore the transformative potential of AI in agriculture. The implementation of AI-based strategies in real-world settings has shown significant improvements in crop yields and water efficiency, demonstrating the practical viability of these technologies. The study concludes that AI has the potential to revolutionize agriculture, enhancing sustainability and productivity in the face of climate challenges.