<p>Agricultural productivity is influenced by various factors, including climatic conditions, soil properties, and environmental factors. Traditional methods of analyzing the agro-climatic factors are based on manual and visual observation. It often fails to capture the intricate interactions that affect the sustainable growth of crops. Moreover, the efficacy of existing machine learning-based methods depends on the tuning of hyperparameters and requires a high level of human intervention. To overcome these issues, a grid search optimized ensemble framework has been proposed to analyze the agro-climatic factors affecting agriculture productivity. The ensemble framework integrates the random forest, gradient boosting, adaptive boosting, K-nearest neighbor, support vector regressor, and extreme gradient boosting in a single framework. These base models are combined using a stacking regressor with linear regression as a meta-model. Additionally, to refine the efficacy of the proposed ensemble framework, grid search is employed to tune the hyperparameters of these base models. To accomplish this, mean squared error is used as an objective fun2ction. Besides this, a Gradio-based graphical user interface is developed for real-time agro-climatic factor assessment. The interface serves as a farming assistance tool and provides an analysis of farming suitability based on the agro-climatic factors. The performance of the proposed method is judged on self-acquired agro-climatic data. The comparative quantitative assessment illustrates that the proposed method outperforms the existing methodologies for the custom agro-climatic data. The proposed method has a performance gain in terms of R² over KNN, RF, XG-Boost, G-Boost, AdaBoost, and SVR with values of + 4.83%, + 4.66%, + 1.78%, + 1.16%, + 1.37%, and + 1.77%. The feature importance analysis of each agro-climatic factor is also evaluated. The quantitative observation reveals that the minimum temperature has the highest impact on crop productivity.</p>