<p>In order to maximize crop recommendations and yield prediction in complex and unpredictable environmental conditions, smart agriculture necessitates intelligent decision-support systems. Traditional approaches face the overfitting issue without proper parameter tuning. Therefore, this research focuses on designing a novel model, namely, the Attention Bidirectional Gated Recurrent-based Crossover Brown Bear algorithm. The proposed model is assisted in learning long-term and bidirectional dependencies by applying two main techniques, such as Bidirectional-Recurrent Neural Network and Gated Recurrent Units. The proposed model generates accurate results by using an attention mechanism, which effectively highlights the highly relevant attributes in the agriculture data. The Crossover Brown Bear algorithm is used for hyperparameter tuning, which improves convergence speed and model robustness, to guarantee effective and stable training. The proposed model’s capabilities are validated on the experimental analyses based on three selected agricultural datasets. The core evaluation parameters quantify the performance of the proposed model, and the assessment among diverse methods ensures the stable performance of the proposed model. It reaches better performance with a greater accuracy of 98.92% and an F1-score of 97.85%, enabling an effective crop health risk assessment. In the ablation study, the remarkable performance obtained by the proposed approach indicates the performance impacts of the core components. The convergence analysis of the study demonstrates that the proposed algorithm outperforms other algorithms by reaching stable performance. Overall, the experimental outcomes provide evidence that the proposed algorithm is more advanced in supporting crop health risk assessment by taking immediate action.</p>