Recently, face generation from natural language descriptions has become one of the key applications of generative models and one of the most challenging tasks in computer vision and remains. This text-to-face synthesis technique has a wide range of potential applications, including photo editing, forensic investigation, and game development. While significant progress has been achieved in English-based implementations, extending this technology to the Arabic language presents unique challenges due to the scarcity of Arabic datasets and the linguistic complexity of the Arabic morphology, syntax, and semantics. In this paper, we integrate GAN-CLS, a lightweight yet effective text-to-image generation framework, with the AraBERT, a pre-trained Arabic language model to enable face synthesis from a single-sentence Arabic text description. To support our experiments, we constructed a novel Arabic text-to-face dataset by translating English context descriptions from the Multi-Modal CelebA-HQ dataset into Arabic using DeepL Translator. The performance of the proposed framework was evaluated using the Fréchet Inception Distance (FID) and the Learned Perceptual Image Patch Similarity (LPIPS) metrics. This study constitutes an initial attempt at generating facial images from Arabic textual descriptions.