Maintaining food security through increased agricultural production is a major concern for decision-makers, especially in areas with arid and semi-arid climatic conditions and limited natural resources. Land suitability prediction for cultivating strategic crops, including wheat, has emerged as a crucial subject for academics, decision-makers, and economists to ensure the sustainability of natural resources. This paper aims to use three land parameters, three physical parameters, four chemical parameters, and a long-term remote sensing index as input factors to produce land suitability maps for wheat cultivation based on five machine learning algorithms (MLAs): ANN, KNN, RF, SVM, and XgbTree, in the Gozlu agricultural enterprise, which is located in a semi-arid region of the Central Anatolian Plateau. To achieve this target, an inventory of 238 appropriateness points for cultivated wheat has been executed over five years, from 2019 to 2023. The outcomes revealed that the texture and available water capacity parameters were the most influential in land suitability prediction. The best performance among the MLAs was achieved by the XgbTree algorithm, which had an accuracy of 0.98 and a kappa coefficient of 0.81. Additionally, the Area Under the Curve (AUC) was 0.90 according the Receiver Operating Characteristics (ROC) curve approach. The results of the study demonstrated an excellent ability of the MLAs to predict land suitability for wheat cultivation in semi-arid climate conditions. This approach can play a significant role in ensuring food security and serves as an important tool for decision-makers in sustainable development. However, we propose that the approach should be examined in comparable climatic conditions with diverse crops to ensure it is a viable solution with widely cases.