The development in technology and science has contributed to a vast volume of data from various agrarian fields to be aggregated in the public domain. Predicting the crop yield based on climate, soil and water parameters has been a potential re- search subject. Therefore an objective arises in integrating the data with agrarian processes for crop enhancement. In this paper, a new hybrid regression-based algorithm, Reinforcement Random Forest is proposed which displays significantly enhanced performance over traditional machine learning techniques like the random forest, decision tree, gradient boosting, artificial neural network and deep Q-learning. The new strategy executes reinforcement learning at every selection of a splitting attribute amid the process of tree construction for the efficient utilization of the available samples. They analyze the variable significance measure to select the most substantial variable for node splitting process in the model development and promotes efficient utilization of training data. This integrated hybrid procedure provides significant enhancement over prevailing strategies specifically for sparse model structures. Besides performing internal cross-validation, the proposed approach requires less parameter tuning, reduces over-fitting, faster calculation and more transparent. The experimented models are evaluated with various assessment metrics like Root Mean Squared Error, Mean Squared Error, Determination Coefficient and Mean Absolute Error. The results obtained delineated that the proposed approach performs better with reduced error measures and improved accuracy of 92.2%. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.