Metaheuristics are a branch of stochastic optimization, which is a method for finding the best answer to a problem. If conventional optimisation strategies—like gradient ascent—are unsuccessful, researchers will typically resort to metaheuristic approaches. If an algorithm is trained to use a metaheuristic approach, it will be able to recognise a good solution when it sees one, even if the algorithm does not know what makes a solution excellent. When the issue space is broad or complicated, these methods shine because they can efficiently test a wide variety of options. Several methods, such as data mining, AI, and machine learning, may be used to make estimates of the bio-ecological quality of soil. Using criteria including texture and segmentation, these methods may examine photographs of soil sections to determine the level of soil fertility. Factors like air and soil temperature are used to determine the optimal soil type for a certain crop. Finding the best answer to this problem may be done with the help of population-based metaheuristic algorithms like evolutionary algorithms, swarm intelligence algorithms, physics-based algorithms, and bio-inspired algorithms. When applied to difficult or massive situations, metaheuristic approaches can improve the efficiency and precision of soil analysis and crop selection.