Recent advances in agricultural analytics allow for AI-driven learning applications. These programs may improve crop monitoring and management. This chapter covers how machine learning has improved crop management and monitoring in agricultural analytics. To meet the global food demand, we need modern technology to boost agricultural productivity and efficiency. Machine learning solves these issues comprehensively. The proposed crop health monitoring and resource management method uses photo recognition and predictive analytics. Several different technologies are employed. We use decision trees and neural networks to evaluate satellite data and predict insect infestations. Decision tree models may improve yield projection accuracy by 25% and pest prediction accuracy by 30%, according to neural network-based image processing studies. Crop management and resource allocation have improved significantly. To ensure food security, agricultural machine learning has changed crop management and monitoring. Machine learning has transformed agriculture. Machine learning may perform better with more data.