Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dependencies of explanatory variables. The authors present the Antlion Optimization Algorithm as a modern strategy to analyzing the structure of a Bayesian network (ALO). In the algorithm; deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. , greedy search, bee with  Simulated Annealing as a Hybrid algorithm, Simulated Annealing, Greedy Search Hybrid Bee, and Pigeon-inspired optimization using the BDe Score function are all contrasted with the proposed solution. Using a variety of reference data sets, the researchers looked at how these methods represented the uncertainty matrix. The results of the tests indicate that the performance is more consistent and has higher score values than the other algorithms.