Artificial intelligence (AI) involves the development of systems capable of performing tasks that typically require human intelligence, including fields such as machine learning, natural language processing, computer vision, robotics, expert systems, and neural networks. Over the past few decades, AI’s integration into agriculture has accelerated, building on the early 2000s’ concept of precision agriculture, which utilized remote sensing, GPS, and yield monitoring for site-specific crop management. Since the 2010s, AI-powered technologies, such as imaging, sensing, and machine learning algorithms, have made it easier for farmers to accurately monitor crop health, identify diseases, and estimate yield potential. Robots and autonomous drones have also revolutionized planting, weeding, and harvesting. The world’s population is expected to rise by two billion by 2050, but agricultural land is only growing by 4%, making efficient cultivation practices urgent. AI represents a standard shift in addressing agricultural challenges such as disease, soil analysis, pest infestation, irrigation, and inadequate drainage, often leading to environmental hazards and crop loss due to excessive chemical use. Despite these obstacles, advancements in AI-driven technology and agricultural robots point to a promising future for AI in agriculture, with the goal of addressing food insecurity and meeting the needs of a growing global population through innovative and environmentally responsible methods.