This study aims at achieving the highest forecast rates of plant growth by carrying out hyperparameter tuning in combination with machine learning ensemble modelling. To achieve a high level of accuracy in developing a forecasting of the outcomes that are expected from the plant, there is reinforced ensemble methodology employed by the research that includes Random Forest, Gradient boosting, as well as XGBoost. After tuning the parameters of these models, we observed an improvement in accuracy to 91% in the ensemble model which signifies significant improvement over the individual models. Better and more reliable predictions are generated out of prudent and complicated, nonlinear relations in the data, thanks to the incorporation of these complex algorithms. Utilizing this technique agricultural practices could be enhanced hence improving the growth conditions and crop yields with better decision making. The results show that hyperparameter tuning and ensemble learning are two powerful techniques in precision agriculture with the ability to give accurate and efficient prediction and monitoring of plant growth.