The rapid advancement of precision agriculture has been propelled by integrating sophisticated machine learning (ML) techniques into crop monitoring and management systems. This chapter examines the most recent developments in utilizing machine learning in precision agriculture. It highlights how these technologies improve decision-making, optimize resource utilization, and enhance crop yields. The chapter commences with an exposition on the basic tenets of precision agriculture and the significance of data-driven methodologies in contemporary farming. The text explores various machine learning algorithms and their practical uses, such as predictive modelling for crop growth, disease detection, yield estimation, and resource management. Case studies and real-world examples demonstrate the tangible advantages of machine learning in diverse agricultural settings, ranging from small-scale farms to large agricultural enterprises. The chapter discusses the obstacles and prospects of incorporating machine learning into precision agriculture. It emphasizes the importance of interdisciplinary cooperation, reliable data management systems, and ongoing innovation to harness these technologies’ capabilities fully.