Effective fertilizer management is crucial to improve agricultural yields while reducing environmental impact. This research presents a new variable rate fertilizer management method that uses cloud computing and Gradient Boosting Machines (GBM). To find the best fertilizer rates to apply, create a system that uses GBM algorithms to analyze data from several sources, such as soil nutrient levels, weather, and crop growth characteristics. The cloud-based platform's real-time data integration and processing enable scalable deployment in various agricultural contexts and continuous model upgrades. By utilizing GBM's predictive capabilities, the proposed system can dynamically adjust fertilizer application rates in response to changing crops and environmental circumstances. The results of the field-testing show that the system greatly increases the efficiency of fertilizer usage, which in turn increases crop yields while decreasing the environmental effect due to the reduction of nutrient runoff. Farmers may scale their operations efficiently using cloud computing and get real-time, actionable information and advice. Precision agriculture and data-driven decision-making are two ways this method promotes sustainable farming practices and increases agricultural production. It demonstrates a game-changing strategy for improving sustainable farming methods and optimizing fertilizer usage. © 2025 Elsevier B.V., All rights reserved.