Accurate evaluation of the Temporomandibular joint (TMJ) components is essential for proper diagnosis and treatment. However, the current diagnostic process relies heavily on manual measurements, which are time-consuming and prone to human error. Here, the fundamental task is performed using imaging techniques and locating regions of interest in the TMJ. Nowadays, such image segmentation has been automated using a number of deep learning models. Nonetheless, developed models for TMJ segmentation are primarily built on symmetric convolutional kernels, which may limit their ability to capture the inherently asymmetric structures of the joint. To address this gap, this work proposes a novel approach that integrates an asymmetric kernel into a DenseUNet architecture and squeeze-and-excitation blocks for the automatic segmentation of three key morphological components of the TMJ. A metaheuristic iterated local search algorithm was used to identify the most effective kernel configuration within a search space of ${15}^2$ asymmetric kernel combinations. The resulting optimized architecture was trained and evaluated on a TMJ imaging dataset and compared against nine state-of-the-art segmentation architectures. The proposed method achieved the highest Dice coefficient of 0.78, outperforming all baseline architectures, and demonstrated efficient processing with an average inference time of 0.16 s per image. These results highlight the potential of the proposed system to enhance the accuracy and efficiency of TMJ diagnostics in clinical settings.