Fertilizer prediction is an important aspect for sustainable and precision agriculture to enhance productivity and better quality yield while promoting environmental and economic health. Integrating AI with agricultural practices ensures that resources are utilized effectively, promoting balanced nutrient management and enhancing crop yield. The proposed Fertilizer Prediction System uses boosting technique-based machine learning algorithms to recommend fertilizer types by analyzing crop and soil conditions. In the work, a thorough investigation on Adaboost algorithm has been done and then a machine learning model is developed by processing system input parameters like soil type, crop type, and nutrient levels, to deliver precise fertilizer recommendations to maximize crop yield and ensure balanced nutrient management. This solution is built using Adaboost algorithm, a boosting principle-based machine learning approach using Python. Dataset has been collected from Kaggle repository. The proposed method produces good result in comparison with other traditional prediction algorithms.