This review emphasizes analyzing the application of bio-inspired algorithms in the agriculture sector. Agriculture is crucial in ensuring global food security, ending hunger, establishing food security, improving nutrition, and promoting sustainable agriculture. Agricultural methods are crucial for farmers looking to boost vegetable, legume, and fruit production while maintaining nutritional quality after harvest. In recent times, bio-inspired computing models have emerged as promising techniques to enhance different aspects of agriculture, including crop yield prediction, pest management, and resource optimization. This paper reviews the application of bio-inspired computing models in the agricultural domain, to consolidate current knowledge, identify trends, and suggest possible avenues for future research. The study presents bio-inspired computing concepts, such as swarm intelligence, artificial neural networks, genetic algorithms, and evolutionary computing. The paper also investigates articles and review papers, offering insights into the successful deployment of bio-inspired computing models in various agricultural applications such as autonomous farming technology, decision support systems, remote sensing, and precision farming. These include the integration of bio-inspired computing models with the use of machine learning techniques and the potential application of bio-inspired computing approaches in the agriculture sector. The paper aims to help develop sustainable and efficient agricultural practices on a global scale.