Precision agriculture has developed to be an essential input for sustainable farming given the two overarching objectives of enhancing agricultural production and promoting environment conservation. This study seeks to fill some of the gaps in crop recommendation systems by presenting the Stacked Ensemble Learning based Intelligent Crop Recommendation System (ICRS). This part links both the soil nutrients—nitrogen, phosphorus, and potassium—to the growth conditions—temperature, humidity, PH level, and rainfall—to provide suitable crop types based on the agro-climatic zones. The aggregation technique adopted in the study comprises four modern tree-based algorithms, namely Random Forest, XGBoost, AdaBoost, and LightGBM arranged in a stacked ensemble format, with high level of tuning from multiple metrics to enhance the prediction error. A comprehensive set of experiments were performed on a large variety of data set which resulted into a fascinating overall classification accuracy of 99.12% over the individual models. Comparing the results with other methods also clarifies how stable and efficient the ensemble approach is in addressing large-scale tasks. The proposed decision-support system increases farmers’ awareness in order to make efficient decisions that lead to high yields and reduced wastage of resources. The study supports how artificial intelligence for agriculture can support the practice of sustainable farming for climate variability and change. The result of this work is more than providing a basic contribution to the emerging science of precision agriculture field but it also provides a global framework that can easily be adopted in different growing conditions across the globe.