The emergence of intelligent techniques has revolutionized traditional approaches in manufacturing. This study explores the effectiveness of a hybrid intelligent method for modeling and optimizing the laser microgrooving process on sapphire. It focuses on examining the impact of three critical laser machining factors (laser fluence, scanning speed, and number of passes) on the geometric properties of the resulting microgrooves. Initially, the experimental dataset is used to train and test Artificial Neural Networks (ANN). Bayesian Optimization Algorithms (BOA) are implemented to fine-tune the hyperparameters of ANN to enhance the accuracy of the model. The results demonstrate the model’s robust capability to predict the depth and width of laser-microgrooved sapphire, providing a deeper insight into the process dynamics. In the subsequent phase, the study leverages the robust modeling prowess of the ANN-BOA and the impressive efficiency of the Improved Grasshopper Optimization Algorithm (IGOA) to produce microgrooves with optimal aspect ratios. In this regard, the well-trained ANN-BOA models are utilized as objective functions in the IGOA. This integration results in optimal processing conditions for fabricating microgrooves with superior aspect ratios. The results show that optimal laser parameters of 3.49 J/cm2 fluence, 544 mm/min scanning speed, and 5 passes can achieve microgrooves on sapphire with a favorable aspect ratio of about 2, demonstrating the efficacy of the hybrid ANN-BOA-IGOA optimization approach. Overall, the study highlights the potential of hybrid intelligent methods in modeling and optimizing complex manufacturing processes.