This paper investigates the role of artificial intelligence (AI) in optimizing carbon farming practices to enhance carbon sequestration and drive climate mitigation. Remote sensing, machine learning, and geospatial analytics were employed in a systematic literature review—conducted in accordance with the PRISMA protocol—to evaluate key soil health indicators, including soil organic carbon levels, crop diversity, and greenhouse gas emissions. It was found that AI-driven techniques, such as real-time monitoring and predictive modeling, substantially improve practices such as no-till farming and biochar application. A concise framework is proposed to demonstrate how the integration of AI into carbon farming can accelerate sustainable practices and contribute to global climate action.