The present project illustrates a comprehensive crop health monitoring and recommendation system using environmental condition data in the context of optimizing agricultural practices. Crop health monitoring utilizes AWS SageMaker Studio in training and deploying a machine learning model to classify crops as either healthy or unhealthy based on environmental inputs like temperature, humidity, rainfall, N, P, K and Ph. A Flask application, developed in SageMaker Studio, is designed as the interface for a real-time crop health prediction tool, giving farmers actionable inputs for timely intervention. Finally, a crop recommendation system, using AWS SageMaker, analyzes the environmental dataset to suggest the most suitable crop for a given region. These systems combined create sustainable farming practices and contribute positively to the improvement of agriculture productivity.