Smart farming is reshaping modern agriculture by combining Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to make farming more efficient, sustainable, and data-driven. As farmers face the growing challenges of climate change, shrinking natural resources, and rising global food demands, technology is becoming an essential partner in improving how crops are planned, monitored, and managed. This study explores how smart farming technologies—such as sensors, drones, and IoT-based monitoring systems—are transforming every stage of agriculture, from soil management to market prediction. It introduces a practical framework built around three key areas: Automated Smart Farming Operations, Farmgate-to-Fork and Data-Driven Decision Support. These systems work together to optimize resources, minimize waste, and boost productivity through predictive insights and real-time data analysis. The paper also examines how AI models like Convolutional Neural Networks (CNNs), Random Forests (RF), and Support Vector Machines (SVM) contribute to precision farming. While the benefits are clear, challenges such as limited data access, high technology costs, and unequal adoption among small farmers remain. Overall, the study highlights how smart farming can lead agriculture toward a more sustainable, inclusive, and technology-driven future.

Keywords: Artificial Intelligence; Machine Learning Soil Management; Crop Production; Market Dynamics; Farmer Empowerment; Precision Agriculture; Explainable AI; Sustainability