Machine learning (ML) is revolutionizing today’s agriculture through the enhancement of yields, management of resources, and sustainability. This chapter reviews recent innovation in ML techniques in crop management with insights to the researcher, practitioner, and policy maker. The chapter initially lays out ML importance in agriculture including key principles such as supervised, unsupervised, and reinforcement learning. It discusses data sources, such as satellite imagery, UAVs, IoT sensors, and smart farming hardware, together with preprocessing techniques such as cloud-based approaches, edge computing, and Generative Adversarial Networks (GANs) for data enhancement. Some of the key areas of discussion are feature engineering for crop modeling, crop yield forecasting using deep learning (RNNs, LSTMs), and AI-based disease and pest detection using convolutional neural networks (CNNs). Management of soil health, climate effect modeling, and predictive irrigation scheduling based on AI are also addressed. Remote sensing integration with ML, deep learning-based crop classification, and AIoT (AI + IoT) in intelligent farming are emphasized. The use of big data analytics, cloud computing (AWS, Google Cloud, Azure), and robotics in automation (sowing, weeding, harvesting) is discussed. The influence of blockchain on agricultural data security and supply chain transparency is also discussed. Lastly, the chapter discusses challenges and future directions, highlighting sustainability, adoption barriers, and policy frameworks. It is a thorough reference on ML-driven precision agriculture, providing practical applications and future developments in crop management.