A wide range of technologies has been implemented into modern agriculture to fulfill the need for food production in agriculture, both in terms of quantity and quality. In this technologically advanced agricultural era, a variety of technological tools have made it possible for farmers to make fast and accurate decisions based on observable data that eventually converge. This has helped farmers overcome numerous challenges related to their farming operations. At present, machine learning has been applied to all parts of agriculture to assist ranchers in making more informed decisions using quantifiable information. This article gives an overview of how AI precision farming and agriculture work. Subsequently, propose a deep learning-based unique crop recommendation platform that will enable farmers to decide what should be harvested as per a list of established factors. I used a Kaggle dataset, and the data was normalized through Z-score normalization, the same to make it normalized. For feature selection, the Ant Lion Optimization (ALO) algorithm was employed, Choosing the most essential features will increase the model’s efficiency. The categorization assignment was performed using a Bi-directional Gated Recurrent Unit (Bi-GRU) model, known for its robust performance in sequence data. Additionally, the Cheetah Optimization Algorithm (COA) was used to fine-tune the hyperparameters of the Bi-GRU model. By achieving a remarkable accuracy of 99.74%, the proposed approach could have a great potential to significantly help in forming decisions on crop recommendations by making precise predictions based on climate, soil, and environmental data.