Abstarct: Kick prediction and blowout control are recognized as critical challenges in the oil and gas industry, significantly impacting operational safety and efficiency. This study investigates the application of artificial neural networks (ANNs) as an innovative and effective method for predicting kick and controlling blowouts. The developed ANN model was trained and tested using data from five wells in southern Iran, where one well was designated for training and the remaining four wells were used for validation. This approach allowed for a thorough assessment of the model’s generalization capability and prediction accuracy under various operational conditions Utilizing a combination of real and synthetic datasets, the developed algorithm achieved an initial accuracy of 85% to 99% in detecting the presence or absence of kick baxsed on input parameters. In the second phase of the study, the neural network demonstrated an accuracy range of 80% to 99% in correctly identifying kick events. The results of this research confirm that artificial neural networks offer a highly effective approach for predicting kick and controlling blowouts, making them a powerful tool for decision-making in the oil and gas industry. Furthermore, this study establishes a foundation for future research in this domain and can contribute to the development of optimized kick control systems .