The application of fertilizers in precise doses is a challenging task for the sustained production of crops worldwide. Due to the scarcity of soil scientists, rural farmers apply fertilizers in a blanket dose that hinders crop growth and yield production. Though several fertilizer recommendation systems have been proposed in the literature, they suffer from major limitations. To overcome the limitations, this paper aims to design a robust and efficient fertilizer recommendation system to suggest precise doses of fertilizer for a targeted crop. The system was designed using a regressor assembly of eight regressors for three major fertilizers: urea, single super phosphate, and muriate of potash for two major crops, potato and paddy, cultivated in three districts, Nadia, Hooghly, and Burdwan, in the state of West Bengal, India. In the first step, the performance of each regressor in the assembly was evaluated in terms of three well-known statistical metrics: R2, RMSE, and MAE, and the best three were selected using Friedman’s rank test. In the second step, to get better accuracy, each of these three regressors was hooked up with one of two nature-inspired hyperparameter optimization algorithms, called Particle Swarm Optimization and Bat. Finally, the outperforming regressor-optimizer couple recommends the precise doses of the fertilizers. Authentic soil health card data, collected from the Dept. of Agriculture, Government of India, was used for training, validation, and testing of the regressors. The experimental results showed that the random forest regressor coupled with the Bat algorithm outperformed the others irrespective of the locations, crops, and fertilizers.