The infusion of Artificial Intelligence (AI) into farming gives traditional farming a facelift by enhancing efficiency, accuracy, and sustainability. Techniques based on or employing AI concepts such as machine learning (ML), deep learning (DL), and remote sensing use these technologies more and more in agricultural mapping, crop monitoring, and environ-mental management. With a focus on land use classification, crop health assessment, irrigation optimization, and agroclimatic risk prediction, this re-view presents recent advances in the applications of AI in agriculture. A few of the most widely used AI methods-often seen in various combinations of Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF) models have exhibited notable potential in automating a good number of processes in agriculture. In conclusion, AI is driving the evolution of intelligent farms, enabling data-driven decisions that optimize resources, enhance crop health, and combat climate impacts. In this paper, the use of AI for disease identification, yield estimation, and precision irrigation was addressed, noting models like CNNs and YOLO frameworks. Regardless, challenges of data scarcity, model generalization, and power constraints persist. Future research has to focus on light-weight AI algorithms, climate-tolerant algorithms, and equitable policies for fostering wider adoption. By closing such gaps, AI can foster sustain-able, efficient, and resilient farming.