The integration of Artificial Intelligence (AI) into agriculture has the potential to revolutionize traditional farming practices, addressing several challenges such as resource optimization, crop management, and sustainability. This research paper explores the transformative role of AI in modern agriculture, focusing on its application in precision farming, crop monitoring, predictive analytics, and autonomous machinery. With the global demand for food production increasing due to population growth, AI-driven technologies offer solutions to enhance productivity, reduce costs, and minimize environmental impact. The study examines AI tools such as machine learning algorithms, computer vision, and robotics, which aid in disease detection, pest control, and irrigation management. Furthermore, the paper highlights the use of AI in predictive modeling for weather patterns, soil health, and yield forecasting, helping farmers make informed decisions. Through a critical analysis of case studies and empirical data, this research underscores how AI not only enhances efficiency but also contributes to sustainable agricultural practices by reducing dependency on chemical inputs and conserving water resources. The paper concludes by discussing the limitations of AI adoption, particularly in developing regions, and suggests recommendations for overcoming these challenges to unlock the full potential of AI in agriculture.